Unsupervised learning of scene structure for synthetic data generation

ABSTRACT

A rule set or scene grammar can be used to generate a scene graph that represents the structure and visual parameters of objects in a scene. A renderer can take this scene graph as input and, with a library of content for assets identified in the scene graph, can generate a synthetic image of a scene that has the desired scene structure without the need for manual placement of any of the objects in the scene. Images or environments synthesized in this way can be used to, for example, generate training data for real world navigational applications, as well as to generate virtual worlds for games or virtual reality experiences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to U.S. Provisional PatentApplication Ser. No. 62/986,614, filed Mar. 6, 2020, and entitled“Bridging the Sim-to-Real Gap: Unsupervised Learning of Scene Structurefor Synthetic Data Generation,” which is hereby incorporated herein inits entirety for all purposes.

BACKGROUND

Applications such as gaming, animation, and simulation are increasinglyrelying upon more detailed and realistic virtual environments. In manyinstances, procedural models are used to synthesize scenes within theseenvironments, as well as to create labeled synthetic datasets formachine learning. In order to produce realistic and diverse scenes, anumber of parameters governing the procedural models must be carefullytuned by experts. These parameters control both the structure of scenesbeing generated (e.g. how many cars in the scene), as well as parametersthat place objects in valid configurations. The complexity and amount ofknowledge to manually determine and tune these parameters, as well as toconfigure other aspects of these scenes, can limit widespread adoption,and can also limit the realism or extent of the environments generated.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIGS. 1A and 1B illustrate images that can be generated, according to atleast one embodiment;

FIGS. 2A, 2B, and 2C illustrate rules and graphs for a scene, accordingto at least one embodiment;

FIGS. 3A, 3B, and 3C illustrate stages of scene graph generation,according to at least one embodiment;

FIG. 4 illustrates a process for generating an image from a scenegrammar, according to at least one embodiment;

FIG. 5 illustrates a process for training a network, according to atleast one embodiment;

FIG. 6 illustrates components of a system for generating a scene graph,according to at least one embodiment;

FIG. 7A illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 7B illustrates inference and/or training logic, according to atleast one embodiment;

FIG. 8 illustrates an example data center system, according to at leastone embodiment;

FIG. 9 illustrates a computer system, according to at least oneembodiment;

FIG. 10 illustrates a computer system, according to at least oneembodiment;

FIG. 11 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 12 illustrates at least portions of a graphics processor, accordingto one or more embodiments;

FIG. 13 is an example data flow diagram for an advanced computingpipeline, in accordance with at least one embodiment;

FIG. 14 is a system diagram for an example system for training,adapting, instantiating and deploying machine learning models in anadvanced computing pipeline, in accordance with at least one embodiment;and

FIGS. 15A and 15B illustrate a data flow diagram for a process to traina machine learning model, as well as client-server architecture toenhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment.

DETAILED DESCRIPTION

Approaches in accordance with various embodiments can provide for thegeneration of synthetic images and datasets. In particular, variousembodiments can generate virtual scenes or environments based at leastin part upon a set of rules that define the placement and appearance ofobjects, or “assets,” within that environment. These datasets can beused for generating virtual environments, as well as for generatinglarge training datasets that are relevant to a target realistic dataset.Synthetic datasets provide an appealing opportunity for training machinelearning models for use in tasks such as perception and planning inautonomous and semi-autonomous driving, indoor scene perception,generative content creation, and robotic control. Via graphics engines,synthetic datasets can provide ground-truth data for tasks in whichlabels are expensive or even impossible to obtain, such as segmentation,depth, or material information. As illustrated in the images 100, 150 ofFIGS. 1A and 1B, this can include ground truth data for objects renderedin those images, such as bounding boxes and labels for automobiles 102,152 and people 104 rendered in those images. Adding a new type of labelto such a synthetic dataset can be performed by making a call to arenderer, rather than embarking on a time-consuming annotation endeavorthat requires new tooling and hiring, training, and overseeingannotators.

Using conventional approaches, creating synthetic datasets comes withvarious hurdles. While content, such as three-dimensional computer-aideddesign (3D CAD) models that make up a scene, can be obtained fromsources such as online asset stores, artists often must write complexprocedural models that synthesize scenes by placing these assets inrealistic layouts. This often requires browsing through massive amountsof real imagery to carefully tune a procedural model, which can be avery time consuming task. For scenarios such as street scenes, creatingsynthetic scenes relevant for one city may require tuning a proceduralmodel made for another city from scratch. Approaches in accordance withvarious embodiments can attempt to provide automated approaches forhandling these and other such tasks.

In one approach, scene parameters in a synthetically-generated scene canbe optimized by exploiting the visual similarity of generated (e.g.,rendered) synthetic data with real data. Scene structure and parameterscan be represented in a scene graph, with data generated by sampling arandom scene structure (and parameters) from a given probabilisticgrammar of scenes, then modifying the scene parameters using a learntmodel. Since such an approach only learns scene parameters, asimulation-to-real gap remains in the scene structure remains. Forexample, one would likely find a higher density of cars, people, andbuildings in Manhattan than in a quaint village in Italy. Other work ongenerative models of structural data such as graphs and grammar stringsrequires large amounts of ground truth data for training to generaterealistic samples. However, scene structures are extremely cumbersome toannotate and thus not available in most real datasets.

Approaches in accordance with various embodiments can utilize aprocedural generative model of synthetic scenes that is learned,unsupervised, from real imagery. In at least one embodiment, one or morescene graphs can be generated object-by-object by learning to samplerule expansions from a given probabilistic scene grammar and generatescene parameters Learning without supervision for such a task can bechallenging, due at least in part to the discrete nature of the scenestructures to be generated and the presence of a non-differentiablerenderer in the generative process. To this end, a feature spacedivergence can be utilized to compare generated (e.g., rendered) sceneswith real scenes, which can be determined for individual scenes. Such anapproach can allow credit assignment for training with reinforcementlearning. Experimentation on two synthetic datasets and a real datasetindicated that an approach in accordance with at least one embodimentsignificantly reduces the distribution gap between scene structures ingenerated and target data, improving over human priors on scenestructure by learning to closely align with target structuredistributions. On a real dataset, starting from minimal human priors,the structural distribution in the real target scenes can be almostexactly recovered, which is notable given that this model may be trainedwithout any labels. An object detector trained on this generated datahas been shown to outperform detectors trained on data generated withhuman priors, for example, and demonstrates improvements in distributionsimilarity measures of generated rendered images with real data.

Instead of running inference per scene as in a prior approach,approaches in accordance with various embodiments can generate new datathat resembles a target distribution. One approach would be to learn tooptimize non-differentiable simulators using a variational upper boundof a GAN-like objective, or to optimize simulator parameters for controltasks by directly comparing real and simulated trajectories. An approachin accordance with at least one embodiment can learn to generatediscrete scene structures constrained to a grammar, while optimizing adistribution matching objective (with Reinforcement Learning) instead ofusing adversarial training. Such an approach can be used to generatelarge and complex scenes, as opposed to images of single objects orfaces.

In at least one embodiment, generative models composed of graphs andtrees can produce graphs with richer structure with more flexibilityover grammar-based models, but may fail to produce syntactically correctgraphs for cases with a defined syntax, such as programs and scenegraphs. Grammar-based methods have been used for a variety of tasks suchas program translation, conditional program generation, grammarinduction, and generative modelling on structures with syntax, such asmolecules. These methods, however, assume access to ground-truth graphstructures for learning. Approaches in accordance with variousembodiments can train a model in an unsupervised fashion, without anyground truth scene graph annotations.

In at least one embodiment, a set of rules can be generated or obtainedfor a virtual scene or environment to be generated. For example, anartist might generate or provide a set of rules to be used for a scene,or may select from a library of rule sets for various scenes. This mayinclude, for example, browsing scene type options through a graphicalinterface and selecting a scene type that has an associated set ofrules, as may correspond to scene types such as European city, Americancountryside, dungeon, and so on. For a given virtual or “synthetic”scene, an artist may also create or obtain content for various objects(e.g., “assets”) in a scene, as may include models, images, textures,and other features that can be used to render those objects. The rulesprovided can indicate how these objects should relate to one another ina given scene.

For example, FIG. 2A illustrates an example rule set 200 that can beutilized in accordance with various embodiments. This rule set caninclude any number of rules, or up to a maximum number in someembodiments. Each rule can define a relationship between at least twotypes of objects to be represented in a synthetic scene. This rule setapplies to a location where there will be roads and sidewalks. Asillustrated in the rule set 200, a road can have lanes according to afirst rule. According to additional rules, that can be a single lane ormultiple lands, and each lane may be associated with a sidewalk and oneor more cars. As illustrated, rules can also define whether a type ofobject can have one or multiple instances of that type of objectassociated with given object type. Another pair of rules indicates thatthere can be one or more people on a sidewalk in this scene.

Such rules can be used to generate one or more scene structures that arerepresentative of a scene to be generated. Two example scene structures230, 260 are illustrated in FIGS. 2B and 2C. In each of thesestructures, a road is illustrated as a main, parent node in ahierarchical tree structure. The rules from the rule set 200, and therelationships defined therein, determine potential parent-childrelationships that can be used to generate different tree structuresthat conform to those relationships. In at least one embodiment, agenerative model can generate these scene structures from the rule setusing an appropriate sampling or selection process. In the structure 230of FIG. 2B, there is a double lane road where each lane has a sidewalk,and there is a car in one of the lanes. There is also a tree and aperson proximate the sidewalk by the lane with the car. In the structure260 of FIG. 2C, there is a single lane road that has three cars and asidewalk, with two people and a tree proximate the sidewalk. As can beseen, these structures represent two different scenes that weregenerated from the same rule set. Such an approach can be used to buildout, supplement, augment, generate, or synthesize a virtual environmentincluding variations of object structure that all adhere to the selectedrule set. Using such an approach, a single rule set can be used togenerate an environment that is as large as desired, with variationsthat can be random or adhere to a variation policy, without a userhaving to manually select or place these objects. Such a method can beused to generate synthetic scenes from real imagery in an unsupervisedfashion. It at least one embodiment, such an approach can learn agenerative model of scene structure, samples from which (with additionalscene parameters) can be rendered to create synthetic images and labels.In at least one embodiment, such an approach can be used to generatesynthetic training data with appropriate labels, using unlabeled realdata. The rule set and unlabeled real data can be provided as input to agenerative model, which can generate a set of diverse scene structures.

Approaches in accordance with at least one embodiment can learn such agenerative model for synthetic scenes. In particular, given a dataset ofreal imagery X_(R), the problem is to create synthetic data D(θ)=(X(θ),Y(θ)) of images X(θ) and labels Y(θ) that is representative of X_(R),where θ represents the parameters of the generative model. Advances ingraphics engines and rendering can be exploited in at least oneembodiment by stipulating that the synthetic data D is the output ofcreating an abstract scene representation, and rendering that scenerepresentation with a graphics engine. Rendering can ensure thatlow-level pixel information in X(θ) (and its corresponding annotationY(θ)) does not need to be modeled. Ensuring the semantic validity ofsampled scenes may require imposing at least some constraints on theirstructure. Scene grammars use a set of rules to greatly reduce the spaceof scenes that can be sampled, making learning a more structured andtractable problem. For example, a scene grammar could explicitly enforcethat a car can only be on a road which then need not be implicitlylearned. Approaches in accordance with various embodiments can leveragethis in part by using probabilistic scene grammars. Scene graphstructures can be sampled from a prior imposed on a probabilisticcontext-free grammar (PCFG), which is referred to herein as a structureprior. Parameters can be sampled for every node in the scene graph froma parameter prior and learned to predict new parameters for each node,keeping the structure intact. Resulting generated scenes therefore comefrom a structure prior (which is context-free) and the learnt parameterdistribution, which can result in a simulation-to-real gap in the scenestructures.

Approaches in accordance with various embodiments can alleviate at leastthis gap by learning a context-dependent structure distributionunsupervised of synthetic scenes from images. In at least oneembodiment, one or more scene graphs can be used as an abstract scenerepresentation, which can be rendered into a corresponding image withlabels. FIGS. 3A through 3C illustrates components that can be used atdifferent stages of such a process. FIG. 3A illustrates a set of logits300 generated from rule samples, where a given sample is used todetermine the next logit. FIG. 3B illustrates a corresponding mask 330that can be utilized in generating a scene graph. In a generativeprocess for a scene graph, the logits and mask are of shape T_(max)×K.In FIG. 3, unpatterned (e.g., solid white) regions represent representsa higher value while pattern-filled regions represent a lower value. Ateach time step, such a process can autoregressively sample a rule andpredict the logits for the next rule conditioned on the sample,capturing context dependencies. The sampling can be used to generate ascene structure 362, as illustrated in FIG. 3C, as well as to determineparameters for nodes of that scene structure. These parameters caninclude, for example, information like location, height, and pose. Theseand other parameters 366 can be sampled and applied for each node in thescene structure, to generate a full scene graph. Such a process can thusutilize sampled rules from the grammar and convert these into a graphstructure. In this example, only objects that are able to be renderedare kept from the full grammar string. Parameters for every node can besampled from a prior, or optionally learnt. A generated scene graph canbe rendered as illustrated. Such a generative model can sequentiallysample expansion rules from a given probabilistic scene grammar togenerate a scene graph which is rendered. This model can be trainedunsupervised and with reinforcement learning, using a feature-matchingbased distribution divergence specifically designed to be amenable tosuch a setting.

Scene graphs can be advantageous in at least some embodiments due totheir ability, in fields such as computer graphics and vision, todescribe scenes in a concise hierarchical manner, where each nodedescribes an object in the scene along with its parameters. Parameterscan relate to aspects such as a 3D asset or pose. Parent-childrelationships can define the parameters of a child node relative to itsparent, enabling straightforward scene editing and manipulation.Additionally, camera, lighting, weather, and other effects can beencoded into the scene graph. Generating corresponding pixels andannotations can amount to placing objects into the scene in a graphicsengine and rendering with the defined parameters.

In at least one embodiment, the set of rules can be defined as a vector,with the vector having a length equal to the number of rules. A networkcan then be used to determine which of these rules to expand, and theserules can be expanded sequentially at different time steps. For eachscene structure to be generated, a categorial distribution can begenerated over all the relevant rules in a set. A generative network canthen sample from this categorical distribution to select the rules touse for this scene, where that categorical distribution can also bemasked such that certain rules are forced to have a probability of zeroso that they are not selected. The network can also infer which rule oroption to expand for each object. In at least one embodiment, thisgenerative model can be a recurrent neural network (RNN). The latentvector that defines the scene can be input to this RNN to sequentiallygenerate and expand the rules for the scene, based on the determinedprobabilities. The RNN travels down the tree, or stack, until all ruleshave been processed (or a maximum number of rules is reached).

In one or more embodiments, each row in FIG. 3A can correspond to asample rule. As illustrated in FIG. 3B, the mask 330 can then be used toindicate to the model which rules are to be expanded at a given timestep. This process can be performed iteratively to generate a validscene description. Further, the relationships between objects in thescene graph also provide geometric constraints for the scene, as theseobjects cannot exist outside a specified relationship, such as a car notbeing able to be positioned outside a lane or on a sidewalk. Theparameters for a node define various visual attributes, such that roadsin the New Zealand countryside will look different than roads in a bigcity in Thailand. In at least some embodiments, there may be ranges setfor these various parameters for certain types of objects, such thatsidewalks only come in certain widths, roads only have up to a limitednumber of lanes, and so on.

These data structures can also be used to perform additional learning.For example, this data can be used downstream to train a model to, forexample, detect cars in captured image data. This structure could beretained with a generated image, for example, to help the model morequickly be able to identify cars based on where they would occur in thescene structure.

In at least one embodiment, a context-free grammar G can be defined as alist of symbols (e.g., terminal and non-terminal) and expansion rules.Non-terminal symbols have at least one expansion rule into a new set ofsymbols. Sampling from a grammar can involve expanding a start symbol(or initial or parent symbol) until only non-terminal symbols remain. Atotal number of expansion rules K can be defined in a grammar G. Scenegrammars can be defined, and strings sampled from the grammarrepresented, using one or more scene graphs. For each scene graph, astructure T can be sampled from the grammar G followed by samplingcorresponding parameters a for every node in the graph. In at least oneembodiment, a convolutional network is used with this scene graph tosample one set of parameters for every single node in the graph.

In various approaches, a generative model can be utilized that hasgraphs constrained by a grammar. In at least one embodiment, a latentvector z can be mapped to unnormalized probabilities over all possiblegrammar rules in an autoregressive manner, using a recurrent neuralnetwork. This can continue for a maximum of T_(max) steps in such anembodiment. In at least one embodiment, one rule r_(t) can be sampled atevery time step, and this rule can be used to predict logits for thenext rule f_(t+1). This allows this model to capture context-dependentrelationships easily, as opposed to the context-free nature of scenegraphs conventional approaches. Given a list of at most T_(max) sampledrules, the corresponding scene graph is generated by treating each ruleexpansion as a node expansion in the graph as illustrated in FIG. 3.

To ensure validity of these sampled rules in each time step t, alast-in-first-out (LIFO) stack of unexpanded non-terminal nodes can bemaintained. Nodes can be popped from the stack and expanded according tothe sampled rule-expansion, with the resulting new non-terminal nodesthen pushed to the stack. When a non-terminal is popped, a mask m_(t)can be created that is of size K, which is 1 for valid rules from thatnon-terminal and 0 otherwise. Given the logits for the next expansionf_(t), the probability of a rule r_(t,k) can be given by:

${p\left( {r_{t} = \left. k \middle| f_{t} \right.} \right)} = \frac{m_{t,k}e^{f_{t \cdot k}}}{\Sigma_{j = 1}^{K}m_{t,j}e^{f_{t,j}}}$

Sampling from this masked multinomial distribution can ensure that onlyvalid rules are sampled as r_(t). Given the logits and sampled rules,(f_(t),r_(t))∀t∈1 . . . T_(max), the probability of the correspondingscene structure T given z can be given by:

${q_{\theta}\left( T \middle| z \right)} = {\sum\limits_{t = 1}^{T_{\max}}{p\left( r_{t} \middle| f_{t} \right)}}$

Putting this all together, images can be generated by sampling a scenestructure T˜q_(θ)(·|z) from the model, followed by sampling parametersfor every node in the scene α˜q(·|T) and rendering an imagev′=R(T,α)˜q_(I). For some v′˜q_(I), with parameters a and structure T,an assumption can be made as given by:

q _(I)(v′|z)=q(α|T)q _(θ)(T|z)

Various training approaches can be utilized for such a generative model.In at least one embodiment, this training can be performed usingvariational inference or by optimizing a measure of distributionsimilarity. Variational inference allows using reconstruction-basedobjectives by introducing an approximate learnt posterior. Usingvariational inference to train such a model may be challenging due atleast in part to the complexity coming from discrete sampling and havinga renderer in the generative process. Moreover, a recognition networkhere may amount to doing inverse graphics—an extremely challengingproblem in itself. In at least one embodiment, a measure of distributionsimilarity of the generated and target data can be optimized.Adversarial training of a generative model can be utilized withreinforcement learning (RL), such as by carefully limiting the capacityof the critic. In at least one embodiment, reinforcement learning can beused to train a discrete generative model of scene graphs. A metric canbe computed for every sample, which can significantly improve theoverall training process.

A generative model can be trained to match the distribution of featuresof the real data in the latent space of some feature extractor ϕ. Thereal feature distribution can be defined by p_(f) s.t F˜p_(f)⇐ ⇒F=ϕ(v)for some v˜p_(I). Similarly, the generated feature distribution can bedefined as given by q_(f)s.t F˜q_(f)⇐ ⇒F=ϕ(v) for some v˜q_(I).Distribution matching can be accomplished in at least one embodiment byapproximately computing p_(f), q_(f) from samples and minimizing the KLdivergence from p_(f) to q_(f). In at least one embodiment, a trainingobjective can be given by:

$\begin{matrix}\min \\\theta\end{matrix}{{KL}\left( {q_{f}{}p_{f}} \right)}$ $\begin{matrix}\min \\\theta\end{matrix}{E_{F \sim q_{f}}\left\lbrack {{\log{q_{f}(F)}} - {\log{p_{f}(F)}}} \right\rbrack}$

Using the feature distribution definition above, an equivalent objectivecan be given by:

$\begin{matrix}\min \\\theta\end{matrix}{E_{v \sim q_{I}}\left\lbrack {{\log{q_{f}\left( {\varphi(v)} \right)}} - {\log\;{p_{f}\left( {\varphi(v)} \right)}}} \right\rbrack}$

The true underlying feature distributions q_(f) and p_(f) can beintractable to compute. In at least one embodiment, approximations{tilde over (q)}_(f)(F) and {tilde over (p)}_(f)(F) can be used,computed using kernel density estimation (KDE). One example approach canlet V={v₁, . . . , v_(l)} and B={v′₁, . . . , v′_(m)} be a batch of realand generated images. Performing KDE with B,V to estimate q_(f), p_(f)yields:

${{\overset{˜}{q}}_{f}(F)} = {\frac{1}{m}{\sum\limits_{j = 1}^{m}{K_{H}\left( {F - {\varphi\left( v_{j}^{\prime} \right)}} \right)}}}$${{\overset{˜}{p}}_{f}(F)} = {\frac{1}{l}{\sum\limits_{j = 1}^{l}{K_{H}\left( {F - {\varphi\left( v_{j} \right)}} \right)}}}$

where K_(H) is the standard multivariate normal kernel with bandwidthmatrix H. Here, H=dI can be used, where d is the dimensionality of thefeature space.

A generative model in accordance with at least one embodiment can make adiscrete (e.g., non-differentiable) choice at each step, such that itcan be advantageous to optimize the objective using reinforcementlearning techniques. Specifically, this can include using the REINFORCEscore function estimator along with a moving average baseline, wherebythe gradients may be given by:

${\nabla_{\theta}\mathcal{L}} \approx {\frac{1}{M}{\sum\limits_{j = 1}^{m}{\left( {{\log{{\overset{˜}{q}}_{f}\left( {\varphi\left( v_{j}^{\prime} \right)} \right)}} - {\log{{\overset{˜}{p}}_{f}\left( {\varphi\left( v_{j}^{\prime} \right)} \right)}}} \right){\nabla_{\theta}\log}\;{q_{I}\left( v_{j}^{\prime} \right)}}}}$

where M is the batch size, {tilde over (q)}f(F) and {tilde over (p)}f(F)are density estimates defined above.

It can be noted that the gradient above requires computing the marginalprobability q_(I)(v′) of a generated image v′, instead of theconditional q_(I)(v′|z). Computing the marginal probability of agenerated image involves an intractable marginalization over the latentvariable z. To circumvent this, a fixed finite number of latent vectorsfrom a set Z can be used that are sampled uniformly, enabling easymarginalization. This translates to:

${q_{\theta}(T)} = {\frac{1}{Z}{\sum\limits_{z \in Z}{q_{\theta}\left( T \middle| Z \right)}}}$q_(I)(v^(′)) = q(α|T)q_(θ)(T|Z)

Such an approach can still provide enough modeling capacity, since thereare only finitely many scene graphs of a maximum length T_(max) that canbe sampled from the grammar. Empirically, using one latent vector may besufficient, as stochasticity in the rule sampling can make up for loststochasticity in the latent space.

In at least one embodiment, pre-training can be an important step. Ahandcrafted prior can be defined on scene structure. For example, asimple prior could be to put one car on one road in a driving scene. Themodel can be pre-trained, at least in part, by sampling strings (e.g.,scene graphs) from the grammar prior, and training the model to maximizethe log-likelihood of these scene graphs. Feature extraction can also bean important step for distribution matching, as the features need tocapture structural scene information such as the number of objects andtheir contextual spatial relationships for effective training.

During training of a model, sampling may result in incomplete stringsgenerated with at most T_(max) steps. Accordingly, a scene graph T canbe repeatedly sampled until its length is at most T_(max) To ensure thatthis does not require too many attempts, the rejection rater_(reject)(F) of a sampled feature F can be recorded as the averagefailed sampling attempts when sampling the single scene graph used togenerate F. A threshold f can be set on r_(reject)(F) to represent themaximum allowable rejections, as well as weight A, which can then beadded to the original loss as may be given by:

=E _(F˜qF)[log q _(f)(F)−log p _(f)(F)+λ1_((ϵ,∞))(r _(reject)(F))]

Empirically, it was found that values of λ=10⁻² and f=1 worked well inat least one embodiment.

Such an approach can provide for unsupervised learning of a generativemodel of synthetic scene structures by optimizing for visual similarityto real data. Inferring scene structures is notoriously hard, even whenannotations are provided. Approaches in accordance with variousembodiments can perform this generative portion without any ground truthinformation. Experiments have verified the ability of such a model tolearn a plausible posterior over scene structures, significantlyimproving over manually-designed priors. Approaches can optimize forboth the scene structure and parameters of a synthetic scene generatorin order to produce satisfactory results.

As mentioned, such an approach to generating diverse scene graphs canenable generation of scenes or environments that mimic the real world,or a target world or environment. Information for this world orenvironment can be learned directly from pixels of example images of thereal or target world. Such an approach may be used to attempt an exactreconstruction, but in many embodiments can allow for the generation ofinfinitely many diverse worlds and environments that may be based atleast in part upon these real or target worlds. A rule set or scenegrammar can be provided that describes a world at a micro level,defining object-specific relationships. Instead of a person having tomanually generate at least a layout for each scene or image, forexample, that person can specify or select rules that can be used toautomatically generate that scene or image. A scene graph in at leastone embodiment can provide a full description of the layout of athree-dimensional world. In at least one embodiment, the recursiveexpansion of rules to generate a scene structure can also be used togenerate a string that provides a complete representation or definitionof the layout of a three-dimensional scene. As mentioned, a generativemodel can be used to perform the expansion and generate the scenestructure. In at least one embodiment, this scene structure can bestored as a JSON file or using another such format. This JSON file canthen be provided as input to a rendering engine for generating an imageor scene. The rendering engine can pull the appropriate asset data foruse in rendering the individual objects.

As mentioned, this rendered data can be used to present a virtualenvironment, such as for a gaming or VR application. This rendering alsocan be used to generate training data for such an application, as wellas other applications such as training models for autonomous orsemi-autonomous machines, such as for vehicle navigation or roboticsimulation. There may be different libraries of assets that can beselected for these renderings, such that environments may be appropriatefor different geographic locations, points in time, etc. Each pixel in arendered image can be labeled to indicate which type of object thatpixel represents. In at least one embodiment, bounding boxes or otherpositional indicators can be generated for each object, as well as adepth determined for each pixel in the 3D scene, the normal at thatpixel location, etc. This information can be extracted from a renderingengine in at least some embodiments by utilizing an appropriaterendering function to extract the data.

In at least one embodiment, an artist can provide a set of assets andselect a set of rules, and an entire virtual environment can begenerated without manual input by that artist. In some embodiments,there may be libraries of assets from which that artist can select. Forexample, an artist could select a scene structure for “Japanese cities”and assets for “Japanese cities” in order to have an environmentgenerated that is based on Japanese cities, including appropriate visualobjects and layouts, but that does not directly correspond or representany Japanese city. In some embodiments, an artist may have an ability toadjust this environment by indicating things that the artist likes ordoes not like. For example, an artist may not want cars on the streetsfor this application. Accordingly, an artist may indicate that theartist does not want cars included, or at least included in specificareas or associated with specific object types, and a new scene graphcan be generated that has removed cars and updated the appropriaterelationships. In some embodiments a user can provide, obtain, utilize,or generate two or more sub-graphs, such as may indicate things the userlikes and things the user does not like. These sub-graphs can then beused to generate new scenes that are more inline with user expectations.Such an approach can enable a user to easily generate virtualenvironments with specific aspects and visual appearance without anyexpert knowledge of the creation process, or need to manually place,move, or adjust objects in a scene, or set of scenes. Such an approachcan enable an average person to become a 3D artist with minimal efforton the part of the person.

FIG. 4 illustrates an example process 400 for generating an image of ascene that can be utilized in accordance with various embodiments. Itshould be understood that for this and other processes presented hereinthat there can be additional, fewer, or alternative steps performed insimilar or alternative order, or at least partially in parallel, withinscope of various embodiments unless otherwise specifically stated. Inthis example, a rule set is determined 402 that is to be used for atleast one scene to be generated. This may include a user generatingthese rules or selecting from a number of rule sets, among other suchoptions. Individual rules in the set can define relationships betweentypes of object in the scene. This rule set can be sampled 404, as maybe based upon determined probabilities, to generate a scene structurethat includes relationships of objects as defined by the rules. In atleast one embodiment, this can include a hierarchical scene structurewith nodes of the hierarchy corresponding to types of objects for thescene. The parameters to be used in rendering each of these objects canbe determined 406, such as by sampling from an appropriate dataset. Ascene graph can then be generated 408 that can be based on the scenestructure but with appropriate parameters applied for the individualnodes or objects. The scene graph can be provided 410, along with anasset library or other source of object content, to a renderer 410 orother object for generating the image or the scene. A rendered image ofthe scene can be received 412 that was rendered based on the determinedscene graph. This rendered image may include object labels, as well asretain the scene structure, if the image is to be used as training dataas discussed herein.

Various approaches can be used to train neural networks discussedherein. For example, a generative model can be trained to analyzeunlabeled images, which may correspond to captured images of real worldsettings. The generative network can then be trained to generate sceneswith similar appearance, layout, and other such aspects. There can beboth real scenes and synthetic scenes. Whenever you pass these scenes toa deep neural network, a set of features can be extracted in that scenethat correspond to positions in a high-dimensional space, such as1,000-dimensional space. This scene can then be thought of as beingcomposed of these points in this high-dimensional space, rather thanpixels in an image. The network can be trained so that features thatcorrespond to synthetic scenes in this feature space align with featuresthat correspond to real scenes. In this way, it may be difficult todifferentiate between features of real and synthetic scenes in featurespace.

In at least one embodiment, this can be accomplished using reinforcementlearning. As mentioned, a goal can be to align two entire datasets, butwithout any data or correlations about specific feature points thatshould be aligned since this is an unsupervised space withoutcorrelations. Since the goal in many situations will not be to generateexact copies of scenes but to generate similar scenes, it can besufficient to align the distributions of feature points in this featurespace. Accordingly, a training procedure can compare the real andsynthetic scenes holistically. In order to evaluate a scene, that scenein feature space can be compared against a distribution of featurepoints for other scenes. In various approaches, given just a comparisonof signals it can be difficult to determine whether a scene is realisticor useful, other than whether the structure was appropriate.Accordingly, a training approach in accordance with at least oneembodiment can extract the signal from every single data point itself,without having to look at the entire dataset. In this way, a signal canbe evaluated for how well a particular scene is aligned to the wholedataset. A likelihood can then be computed that a particular sceneresolves to all the synthetic scenes. That can provide a likelihood thatthis particular scene is synthetic. This can be performed in at leastone embodiment by using kernel density estimation (KDE). KDE can be usedto obtain a probability that this scene belongs to the distribution ofsynthetic scenes. KDE can also be used to compute the probability thatthis scene belongs to the distribution of real scenes. In at least oneembodiment, a ratio of these values can be analyzed, and the system canbe optimized using this ratio. Maximizing (the log of) this ratio as thereward function for a scene provides a signal that can be optimized forevery single scene.

FIG. 5 illustrates an example process 500 for training a network togenerate realistic images that can be utilized in accordance with atleast one embodiment. In this example, a scene graph and assets areobtained 502, such as described above with respect to FIG. 4. The scenegraph and assets can be used to generate 504 a synthetic image of ascene. A location of a feature point in an n-dimensional feature spacecan be determined 506 for this generated image, where n can equal anumber of rules in a set used to generate the scene graph. This featurepoint for the generated image can be compared 508 against a distributionof feature points for synthetic images in that feature space. A firstprobability can be determined 510 that this generated image is syntheticbased on the comparison. The feature point for the generated image canalso be compared 512 against a distribution of feature points for realimages in that feature space. A second probability can be determined 514that this generated image is realistic based on the comparison. A ratioof these two probabilities can be calculated 516, and one or moreweights for the network being trained can be adjusted in order tooptimize for this ratio.

Another embodiment could utilize a discriminator of a GAN. The GAN couldbe trained to determine whether a generated scene is realistic, usingthe discriminator portion. The network can then be optimized so that thediscriminator determines with high probability that a scene is real.Such an approach may be challenging, however, as current renderersgenerate high quality images but these images can still be identified asnot being real, captured images, such that a discriminator maypredominantly be able to tell the difference even though the images maybe structurally very similar. In such an instance, a GAN might collapseduring training because the discriminator cannot provide any valuableinformation because the rendered image will never confuse thediscriminator as being a real image. In at least one embodiment,image-to-image translation can be performed before providing this imagedata to a GAN to try to improve the appearance of these synthesizedimages. Image-to-image translation can help to reduce the style gapbetween real and synthetic images, helping with low level visual aspectsas may relate to textures or reflections that can cause an image toappear synthetic instead of real. This may be advantageous for systemsthat utilize ray tracing, for example, to generate reflections and otherlighting effects.

In another embodiment, a process can be used to ensure termination. Aneural network can be defined to run through a certain number of steps,such as 150 steps for computational reasons. It is possible that thisnumber will be too low to completely generate the scene graph based on alarger number of rules to be analyzed and expanded. Thus, the generatedscene graph would be incomplete and would result in an inaccuraterendering. In at least one embodiment, a network can be allowed to runto its limit. If the limit is insufficient for a scene, the remainingfeatures can be determined, and a negative reward applied for the modelto ever generate that feature again. Such an approach may result in ascene that does not include all features that were originally desired,but ensures that a scene can be rendered that matches rendering limits.

In at least one embodiment, a client device 602 can generate content fora session using components of a content application 604 on client device602 and data stored locally on that client device. In at least oneembodiment, a content application 624 (e.g., an image generation orediting application) executing on content server 620 may initiate asession associated with at least client device 602, as may utilize asession manager and user data stored in a user database 634, and cancause content 632 to be determined by a content manager 626 and renderedusing a rendering engine, if needed for this type of content orplatform, and transmitted to client device 602 using an appropriatetransmission manager 622 to send by download, streaming, or another suchtransmission channel. In at least one embodiment, this content 632 caninclude assets that can be used by a rendering engine to render a scenebased on a determined scene graph. In at least one embodiment, clientdevice 602 receiving this content can provide this content to acorresponding content application 604, which may also or alternativelyinclude a rendering engine for rendering at least some of this contentfor presentation via client device 602, such as image or video contentthrough a display 606 and audio, such as sounds and music, through atleast one audio playback device 608, such as speakers or headphones. Inat least one embodiment, at least some of this content may already bestored on, rendered on, or accessible to client device 602 such thattransmission over network 640 is not required for at least that portionof content, such as where that content may have been previouslydownloaded or stored locally on a hard drive or optical disk. In atleast one embodiment, a transmission mechanism such as data streamingcan be used to transfer this content from server 620, or contentdatabase 634, to client device 602. In at least one embodiment, at leasta portion of this content can be obtained or streamed from anothersource, such as a third party content service 660 that may also includea content application 662 for generating or providing content. In atleast one embodiment, portions of this functionality can be performedusing multiple computing devices, or multiple processors within one ormore computing devices, such as may include a combination of CPUs andGPUs.

In at least one embodiment, content application 624 includes a contentmanager 626 that can determine or analyze content before this content istransmitted to client device 602. In at least one embodiment, contentmanager 626 can also include, or work with, other components that areable to generate, modify, or enhance content to be provided. In at leastone embodiment, this can include a rendering engine for rendering imageor video content. In at least one embodiment, a scene graph generationcomponent 628 can be used to generate a scene graph from a rule set andother such data. In at least one embodiment, an image generationcomponent 630, which can also include a neural network, can generate animage from this scene graph. In at least one embodiment, content manager626 can then cause this generated image to be transmitted to clientdevice 602. In at least one embodiment, a content application 604 onclient device 602 may also include components such as a renderingengine, scene graph generator 612, and image generation module 614, suchthat any or all of this functionality can additionally, oralternatively, be performed on client device 602. In at least oneembodiment, a content application 662 on a third party content servicesystem 660 can also include such functionality. In at least oneembodiment, locations where at least some of this functionality isperformed may be configurable, or may depend upon factors such as a typeof client device 602 or availability of a network connection withappropriate bandwidth, among other such factors. In at least oneembodiment, a system for content generation can include any appropriatecombination of hardware and software in one or more locations. In atleast one embodiment, generated image or video content of one or moreresolutions can also be provided, or made available, to other clientdevices 650, such as for download or streaming from a media sourcestoring a copy of that image or video content. In at least oneembodiment, this may include transmitting images of game content for amultiplayer game, where different client devices may display thatcontent at different resolutions, including one or moresuper-resolutions.

In this example, these client devices can include any appropriatecomputing devices, as may include a desktop computer, notebook computer,set-top box, streaming device, gaming console, smartphone, tabletcomputer, VR headset, AR goggles, wearable computer, or a smarttelevision. Each client device can submit a request across at least onewired or wireless network, as may include the Internet, an Ethernet, alocal area network (LAN), or a cellular network, among other suchoptions. In this example, these requests can be submitted to an addressassociated with a cloud provider, who may operate or control one or moreelectronic resources in a cloud provider environment, such as mayinclude a data center or server farm. In at least one embodiment, therequest may be received or processed by at least one edge server, thatsits on a network edge and is outside at least one security layerassociated with the cloud provider environment. In this way, latency canbe reduced by enabling the client devices to interact with servers thatare in closer proximity, while also improving security of resources inthe cloud provider environment.

In at least one embodiment, such a system can be used for performinggraphical rendering operations. In other embodiments, such a system canbe used for other purposes, such as for performing simulation operationsto test or validate autonomous machine applications, or for performingdeep learning operations. In at least one embodiment, such a system canbe implemented using an edge device, or may incorporate one or moreVirtual Machines (VMs). In at least one embodiment, such a system can beimplemented at least partially in a data center or at least partiallyusing cloud computing resources.

Inference and Training Logic

FIG. 7A illustrates inference and/or training logic 715 used to performinferencing and/or training operations associated with one or moreembodiments. Details regarding inference and/or training logic 715 areprovided below in conjunction with FIGS. 7A and/or 7B.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, code and/or data storage 701 to storeforward and/or output weight and/or input/output data, and/or otherparameters to configure neurons or layers of a neural network trainedand/or used for inferencing in aspects of one or more embodiments. In atleast one embodiment, training logic 715 may include, or be coupled tocode and/or data storage 701 to store graph code or other software tocontrol timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs). In at least one embodiment, code, such as graph code, loadsweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which the code corresponds. In atleast one embodiment, code and/or data storage 701 stores weightparameters and/or input/output data of each layer of a neural networktrained or used in conjunction with one or more embodiments duringforward propagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, any portion of code and/or data storage 701 maybe included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 701may be internal or external to one or more processors or other hardwarelogic devices or circuits. In at least one embodiment, code and/or codeand/or data storage 701 may be cache memory, dynamic randomlyaddressable memory (“DRAM”), static randomly addressable memory(“SRAM”), non-volatile memory (e.g., Flash memory), or other storage. Inat least one embodiment, choice of whether code and/or code and/or datastorage 701 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, a code and/or data storage 705 to storebackward and/or output weight and/or input/output data corresponding toneurons or layers of a neural network trained and/or used forinferencing in aspects of one or more embodiments. In at least oneembodiment, code and/or data storage 705 stores weight parameters and/orinput/output data of each layer of a neural network trained or used inconjunction with one or more embodiments during backward propagation ofinput/output data and/or weight parameters during training and/orinferencing using aspects of one or more embodiments. In at least oneembodiment, training logic 715 may include, or be coupled to code and/ordata storage 705 to store graph code or other software to control timingand/or order, in which weight and/or other parameter information is tobe loaded to configure, logic, including integer and/or floating pointunits (collectively, arithmetic logic units (ALUs). In at least oneembodiment, code, such as graph code, loads weight or other parameterinformation into processor ALUs based on an architecture of a neuralnetwork to which the code corresponds. In at least one embodiment, anyportion of code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory. In at least one embodiment, any portion of codeand/or data storage 705 may be internal or external to on one or moreprocessors or other hardware logic devices or circuits. In at least oneembodiment, code and/or data storage 705 may be cache memory, DRAM,SRAM, non-volatile memory (e.g., Flash memory), or other storage. In atleast one embodiment, choice of whether code and/or data storage 705 isinternal or external to a processor, for example, or comprised of DRAM,SRAM, Flash or some other storage type may depend on available storageon-chip versus off-chip, latency requirements of training and/orinferencing functions being performed, batch size of data used ininferencing and/or training of a neural network, or some combination ofthese factors.

In at least one embodiment, code and/or data storage 701 and code and/ordata storage 705 may be separate storage structures. In at least oneembodiment, code and/or data storage 701 and code and/or data storage705 may be same storage structure. In at least one embodiment, codeand/or data storage 701 and code and/or data storage 705 may bepartially same storage structure and partially separate storagestructures. In at least one embodiment, any portion of code and/or datastorage 701 and code and/or data storage 705 may be included with otheron-chip or off-chip data storage, including a processor's L1, L2, or L3cache or system memory.

In at least one embodiment, inference and/or training logic 715 mayinclude, without limitation, one or more arithmetic logic unit(s)(“ALU(s)”) 710, including integer and/or floating point units, toperform logical and/or mathematical operations based, at least in parton, or indicated by, training and/or inference code (e.g., graph code),a result of which may produce activations (e.g., output values fromlayers or neurons within a neural network) stored in an activationstorage 720 that are functions of input/output and/or weight parameterdata stored in code and/or data storage 701 and/or code and/or datastorage 705. In at least one embodiment, activations stored inactivation storage 720 are generated according to linear algebraic andor matrix-based mathematics performed by ALU(s) 710 in response toperforming instructions or other code, wherein weight values stored incode and/or data storage 705 and/or code and/or data storage 701 areused as operands along with other values, such as bias values, gradientinformation, momentum values, or other parameters or hyperparameters,any or all of which may be stored in code and/or data storage 705 orcode and/or data storage 701 or another storage on or off-chip.

In at least one embodiment, ALU(s) 710 are included within one or moreprocessors or other hardware logic devices or circuits, whereas inanother embodiment, ALU(s) 710 may be external to a processor or otherhardware logic device or circuit that uses them (e.g., a co-processor).In at least one embodiment, ALUs 710 may be included within aprocessor's execution units or otherwise within a bank of ALUsaccessible by a processor's execution units either within same processoror distributed between different processors of different types (e.g.,central processing units, graphics processing units, fixed functionunits, etc.). In at least one embodiment, code and/or data storage 701,code and/or data storage 705, and activation storage 720 may be on sameprocessor or other hardware logic device or circuit, whereas in anotherembodiment, they may be in different processors or other hardware logicdevices or circuits, or some combination of same and differentprocessors or other hardware logic devices or circuits. In at least oneembodiment, any portion of activation storage 720 may be included withother on-chip or off-chip data storage, including a processor's L1, L2,or L3 cache or system memory. Furthermore, inferencing and/or trainingcode may be stored with other code accessible to a processor or otherhardware logic or circuit and fetched and/or processed using aprocessor's fetch, decode, scheduling, execution, retirement and/orother logical circuits.

In at least one embodiment, activation storage 720 may be cache memory,DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage.In at least one embodiment, activation storage 720 may be completely orpartially within or external to one or more processors or other logicalcircuits. In at least one embodiment, choice of whether activationstorage 720 is internal or external to a processor, for example, orcomprised of DRAM, SRAM, Flash or some other storage type may depend onavailable storage on-chip versus off-chip, latency requirements oftraining and/or inferencing functions being performed, batch size ofdata used in inferencing and/or training of a neural network, or somecombination of these factors. In at least one embodiment, inferenceand/or training logic 715 illustrated in FIG. 7a may be used inconjunction with an application-specific integrated circuit (“ASIC”),such as Tensorflow® Processing Unit from Google, an inference processingunit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processorfrom Intel Corp. In at least one embodiment, inference and/or traininglogic 715 illustrated in FIG. 7a may be used in conjunction with centralprocessing unit (“CPU”) hardware, graphics processing unit (“GPU”)hardware or other hardware, such as field programmable gate arrays(“FPGAs”).

FIG. 7b illustrates inference and/or training logic 715, according to atleast one or more embodiments. In at least one embodiment, inferenceand/or training logic 715 may include, without limitation, hardwarelogic in which computational resources are dedicated or otherwiseexclusively used in conjunction with weight values or other informationcorresponding to one or more layers of neurons within a neural network.In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7b may be used in conjunction with anapplication-specific integrated circuit (ASIC), such as Tensorflow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 715illustrated in FIG. 7b may be used in conjunction with centralprocessing unit (CPU) hardware, graphics processing unit (GPU) hardwareor other hardware, such as field programmable gate arrays (FPGAs). In atleast one embodiment, inference and/or training logic 715 includes,without limitation, code and/or data storage 701 and code and/or datastorage 705, which may be used to store code (e.g., graph code), weightvalues and/or other information, including bias values, gradientinformation, momentum values, and/or other parameter or hyperparameterinformation. In at least one embodiment illustrated in FIG. 7b , each ofcode and/or data storage 701 and code and/or data storage 705 isassociated with a dedicated computational resource, such ascomputational hardware 702 and computational hardware 706, respectively.In at least one embodiment, each of computational hardware 702 andcomputational hardware 706 comprises one or more ALUs that performmathematical functions, such as linear algebraic functions, only oninformation stored in code and/or data storage 701 and code and/or datastorage 705, respectively, result of which is stored in activationstorage 720.

In at least one embodiment, each of code and/or data storage 701 and 705and corresponding computational hardware 702 and 706, respectively,correspond to different layers of a neural network, such that resultingactivation from one “storage/computational pair 701/702” of code and/ordata storage 701 and computational hardware 702 is provided as an inputto “storage/computational pair 705/706” of code and/or data storage 705and computational hardware 706, in order to mirror conceptualorganization of a neural network. In at least one embodiment, each ofstorage/computational pairs 701/702 and 705/706 may correspond to morethan one neural network layer. In at least one embodiment, additionalstorage/computation pairs (not shown) subsequent to or in parallel withstorage computation pairs 701/702 and 705/706 may be included ininference and/or training logic 715.

Data Center

FIG. 8 illustrates an example data center 800, in which at least oneembodiment may be used. In at least one embodiment, data center 800includes a data center infrastructure layer 810, a framework layer 820,a software layer 830, and an application layer 840.

In at least one embodiment, as shown in FIG. 8, data centerinfrastructure layer 810 may include a resource orchestrator 812,grouped computing resources 814, and node computing resources (“nodeC.R.s”) 816(1)-816(N), where “N” represents any whole, positive integer.In at least one embodiment, node C.R.s 816(1)-816(N) may include, butare not limited to, any number of central processing units (“CPUs”) orother processors (including accelerators, field programmable gate arrays(FPGAs), graphics processors, etc.), memory devices (e.g., dynamicread-only memory), storage devices (e.g., solid state or disk drives),network input/output (“NW I/O”) devices, network switches, virtualmachines (“VMs”), power modules, and cooling modules, etc. In at leastone embodiment, one or more node C.R.s from among node C.R.s816(1)-816(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in data centers at various geographicallocations (also not shown). Separate groupings of node C.R.s withingrouped computing resources 814 may include grouped compute, network,memory or storage resources that may be configured or allocated tosupport one or more workloads. In at least one embodiment, several nodeC.R.s including CPUs or processors may grouped within one or more racksto provide compute resources to support one or more workloads. In atleast one embodiment, one or more racks may also include any number ofpower modules, cooling modules, and network switches, in anycombination.

In at least one embodiment, resource orchestrator 812 may configure orotherwise control one or more node C.R.s 816(1)-816(N) and/or groupedcomputing resources 814. In at least one embodiment, resourceorchestrator 812 may include a software design infrastructure (“SDI”)management entity for data center 800. In at least one embodiment,resource orchestrator may include hardware, software or some combinationthereof.

In at least one embodiment, as shown in FIG. 8, framework layer 820includes a job scheduler 822, a configuration manager 824, a resourcemanager 826 and a distributed file system 828. In at least oneembodiment, framework layer 820 may include a framework to supportsoftware 832 of software layer 830 and/or one or more application(s) 842of application layer 840. In at least one embodiment, software 832 orapplication(s) 842 may respectively include web-based service softwareor applications, such as those provided by Amazon Web Services, GoogleCloud and Microsoft Azure. In at least one embodiment, framework layer820 may be, but is not limited to, a type of free and open-sourcesoftware web application framework such as Apache Spark™ (hereinafter“Spark”) that may utilize distributed file system 828 for large-scaledata processing (e.g., “big data”). In at least one embodiment, jobscheduler 822 may include a Spark driver to facilitate scheduling ofworkloads supported by various layers of data center 800. In at leastone embodiment, configuration manager 824 may be capable of configuringdifferent layers such as software layer 830 and framework layer 820including Spark and distributed file system 828 for supportinglarge-scale data processing. In at least one embodiment, resourcemanager 826 may be capable of managing clustered or grouped computingresources mapped to or allocated for support of distributed file system828 and job scheduler 822. In at least one embodiment, clustered orgrouped computing resources may include grouped computing resource 814at data center infrastructure layer 810. In at least one embodiment,resource manager 826 may coordinate with resource orchestrator 812 tomanage these mapped or allocated computing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 828 of framework layer 820. The one or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 828 of framework layer 820. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) orother machine learning applications used in conjunction with one or moreembodiments.

In at least one embodiment, any of configuration manager 824, resourcemanager 826, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a data center operator ofdata center 800 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adata center.

In at least one embodiment, data center 800 may include tools, services,software or other resources to train one or more machine learning modelsor predict or infer information using one or more machine learningmodels according to one or more embodiments described herein. Forexample, in at least one embodiment, a machine learning model may betrained by calculating weight parameters according to a neural networkarchitecture using software and computing resources described above withrespect to data center 800. In at least one embodiment, trained machinelearning models corresponding to one or more neural networks may be usedto infer or predict information using resources described above withrespect to data center 800 by using weight parameters calculated throughone or more training techniques described herein.

In at least one embodiment, data center may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, or otherhardware to perform training and/or inferencing using above-describedresources. Moreover, one or more software and/or hardware resourcesdescribed above may be configured as a service to allow users to trainor performing inferencing of information, such as image recognition,speech recognition, or other artificial intelligence services.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 8 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

Computer Systems

FIG. 9 is a block diagram illustrating an exemplary computer system,which may be a system with interconnected devices and components, asystem-on-a-chip (SOC) or some combination thereof 900 formed with aprocessor that may include execution units to execute an instruction,according to at least one embodiment. In at least one embodiment,computer system 900 may include, without limitation, a component, suchas a processor 902 to employ execution units including logic to performalgorithms for process data, in accordance with present disclosure, suchas in embodiment described herein. In at least one embodiment, computersystem 900 may include processors, such as PENTIUM® Processor family,Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel®Nervana™ microprocessors available from Intel Corporation of SantaClara, Calif., although other systems (including PCs having othermicroprocessors, engineering workstations, set-top boxes and like) mayalso be used. In at least one embodiment, computer system 900 mayexecute a version of WINDOWS' operating system available from MicrosoftCorporation of Redmond, Wash., although other operating systems (UNIXand Linux for example), embedded software, and/or graphical userinterfaces, may also be used.

Embodiments may be used in other devices such as handheld devices andembedded applications. Some examples of handheld devices includecellular phones, Internet Protocol devices, digital cameras, personaldigital assistants (“PDAs”), and handheld PCs. In at least oneembodiment, embedded applications may include a microcontroller, adigital signal processor (“DSP”), system on a chip, network computers(“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”)switches, or any other system that may perform one or more instructionsin accordance with at least one embodiment.

In at least one embodiment, computer system 900 may include, withoutlimitation, processor 902 that may include, without limitation, one ormore execution units 908 to perform machine learning model trainingand/or inferencing according to techniques described herein. In at leastone embodiment, computer system 900 is a single processor desktop orserver system, but in another embodiment computer system 900 may be amultiprocessor system. In at least one embodiment, processor 902 mayinclude, without limitation, a complex instruction set computer (“CISC”)microprocessor, a reduced instruction set computing (“RISC”)microprocessor, a very long instruction word (“VLIW”) microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, for example. In atleast one embodiment, processor 902 may be coupled to a processor bus910 that may transmit data signals between processor 902 and othercomponents in computer system 900.

In at least one embodiment, processor 902 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 904. In atleast one embodiment, processor 902 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 902. Other embodiments may alsoinclude a combination of both internal and external caches depending onparticular implementation and needs. In at least one embodiment,register file 906 may store different types of data in various registersincluding, without limitation, integer registers, floating pointregisters, status registers, and instruction pointer register.

In at least one embodiment, execution unit 908, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 902. In at least one embodiment, processor 902 mayalso include a microcode (“ucode”) read only memory (“ROM”) that storesmicrocode for certain macro instructions. In at least one embodiment,execution unit 908 may include logic to handle a packed instruction set909. In at least one embodiment, by including packed instruction set 909in an instruction set of a general-purpose processor 902, along withassociated circuitry to execute instructions, operations used by manymultimedia applications may be performed using packed data in ageneral-purpose processor 902. In one or more embodiments, manymultimedia applications may be accelerated and executed more efficientlyby using full width of a processor's data bus for performing operationson packed data, which may eliminate need to transfer smaller units ofdata across processor's data bus to perform one or more operations onedata element at a time.

In at least one embodiment, execution unit 908 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system 900may include, without limitation, a memory 920. In at least oneembodiment, memory 920 may be implemented as a Dynamic Random AccessMemory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device,flash memory device, or other memory device. In at least one embodiment,memory 920 may store instruction(s) 919 and/or data 921 represented bydata signals that may be executed by processor 902.

In at least one embodiment, system logic chip may be coupled toprocessor bus 910 and memory 920. In at least one embodiment, systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 916, and processor 902 may communicate with MCH 916 viaprocessor bus 910. In at least one embodiment, MCH 916 may provide ahigh bandwidth memory path 918 to memory 920 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 916 may direct data signals between processor902, memory 920, and other components in computer system 900 and tobridge data signals between processor bus 910, memory 920, and a systemI/O 922. In at least one embodiment, system logic chip may provide agraphics port for coupling to a graphics controller. In at least oneembodiment, MCH 916 may be coupled to memory 920 through a highbandwidth memory path 918 and graphics/video card 912 may be coupled toMCH 916 through an Accelerated Graphics Port (“AGP”) interconnect 914.

In at least one embodiment, computer system 900 may use system I/O 922that is a proprietary hub interface bus to couple MCH 916 to I/Ocontroller hub (“ICH”) 930. In at least one embodiment, ICH 930 mayprovide direct connections to some I/O devices via a local I/O bus. Inat least one embodiment, local I/O bus may include, without limitation,a high-speed I/O bus for connecting peripherals to memory 920, chipset,and processor 902. Examples may include, without limitation, an audiocontroller 929, a firmware hub (“flash BIOS”) 928, a wirelesstransceiver 926, a data storage 924, a legacy I/O controller 923containing user input and keyboard interfaces 925, a serial expansionport 927, such as Universal Serial Bus (“USB”), and a network controller934. Data storage 924 may comprise a hard disk drive, a floppy diskdrive, a CD-ROM device, a flash memory device, or other mass storagedevice.

In at least one embodiment, FIG. 9 illustrates a system, which includesinterconnected hardware devices or “chips”, whereas in otherembodiments, FIG. 9 may illustrate an exemplary System on a Chip(“SoC”). In at least one embodiment, devices may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of computer system 900 are interconnected using computeexpress link (CXL) interconnects.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 9 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

FIG. 10 is a block diagram illustrating an electronic device 1000 forutilizing a processor 1010, according to at least one embodiment. In atleast one embodiment, electronic device 1000 may be, for example andwithout limitation, a notebook, a tower server, a rack server, a bladeserver, a laptop, a desktop, a tablet, a mobile device, a phone, anembedded computer, or any other suitable electronic device.

In at least one embodiment, system 1000 may include, without limitation,processor 1010 communicatively coupled to any suitable number or kind ofcomponents, peripherals, modules, or devices. In at least oneembodiment, processor 1010 coupled using a bus or interface, such as a1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus,a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”)bus, a Serial Advance Technology Attachment (“SATA”) bus, a UniversalSerial Bus (“USB”) (versions 1, 2, 3), or a Universal AsynchronousReceiver/Transmitter (“UART”) bus. In at least one embodiment, FIG. 10illustrates a system, which includes interconnected hardware devices or“chips”, whereas in other embodiments, FIG. 10 may illustrate anexemplary System on a Chip (“SoC”). In at least one embodiment, devicesillustrated in FIG. 10 may be interconnected with proprietaryinterconnects, standardized interconnects (e.g., PCIe) or somecombination thereof. In at least one embodiment, one or more componentsof FIG. 10 are interconnected using compute express link (CXL)interconnects.

In at least one embodiment, FIG. 10 may include a display 1024, a touchscreen 1025, a touch pad 1030, a Near Field Communications unit (“NFC”)1045, a sensor hub 1040, a thermal sensor 1046, an Express Chipset(“EC”) 1035, a Trusted Platform Module (“TPM”) 1038, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 such as a SolidState Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local areanetwork unit (“WLAN”) 1050, a Bluetooth unit 1052, a Wireless Wide AreaNetwork unit (“WWAN”) 1056, a Global Positioning System (GPS) 1055, acamera (“USB 3.0 camera”) 1054 such as a USB 3.0 camera, and/or a LowPower Double Data Rate (“LPDDR”) memory unit (“LPDDR3”) 1015 implementedin, for example, LPDDR3 standard. These components may each beimplemented in any suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 1010 through components discussed above. In atleast one embodiment, an accelerometer 1041, Ambient Light Sensor(“ALS”) 1042, compass 1043, and a gyroscope 1044 may be communicativelycoupled to sensor hub 1040. In at least one embodiment, thermal sensor1039, a fan 1037, a keyboard 1046, and a touch pad 1030 may becommunicatively coupled to EC 1035. In at least one embodiment, speaker1063, headphones 1064, and microphone (“mic”) 1065 may becommunicatively coupled to an audio unit (“audio codec and class d amp”)1062, which may in turn be communicatively coupled to DSP 1060. In atleast one embodiment, audio unit 1064 may include, for example andwithout limitation, an audio coder/decoder (“codec”) and a class Damplifier. In at least one embodiment, SIM card (“SIM”) 1057 may becommunicatively coupled to WWAN unit 1056. In at least one embodiment,components such as WLAN unit 1050 and Bluetooth unit 1052, as well asWWAN unit 1056 may be implemented in a Next Generation Form Factor(“NGFF”).

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7a and/or 7 b. In at least one embodiment,inference and/or training logic 715 may be used in system FIG. 10 forinferencing or predicting operations based, at least in part, on weightparameters calculated using neural network training operations, neuralnetwork functions and/or architectures, or neural network use casesdescribed herein.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

FIG. 11 is a block diagram of a processing system, according to at leastone embodiment. In at least one embodiment, system 1100 includes one ormore processors 1102 and one or more graphics processors 1108, and maybe a single processor desktop system, a multiprocessor workstationsystem, or a server system having a large number of processors 1102 orprocessor cores 1107. In at least one embodiment, system 1100 is aprocessing platform incorporated within a system-on-a-chip (SoC)integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 1100 can include, or be incorporatedwithin a server-based gaming platform, a game console, including a gameand media console, a mobile gaming console, a handheld game console, oran online game console. In at least one embodiment, system 1100 is amobile phone, smart phone, tablet computing device or mobile Internetdevice. In at least one embodiment, processing system 1100 can alsoinclude, couple with, or be integrated within a wearable device, such asa smart watch wearable device, smart eyewear device, augmented realitydevice, or virtual reality device. In at least one embodiment,processing system 1100 is a television or set top box device having oneor more processors 1102 and a graphical interface generated by one ormore graphics processors 1108.

In at least one embodiment, one or more processors 1102 each include oneor more processor cores 1107 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 1107 is configuredto process a specific instruction set 1109. In at least one embodiment,instruction set 1109 may facilitate Complex Instruction Set Computing(CISC), Reduced Instruction Set Computing (RISC), or computing via aVery Long Instruction Word (VLIW). In at least one embodiment, processorcores 1107 may each process a different instruction set 1109, which mayinclude instructions to facilitate emulation of other instruction sets.In at least one embodiment, processor core 1107 may also include otherprocessing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor 1102 includes cache memory 1104.In at least one embodiment, processor 1102 can have a single internalcache or multiple levels of internal cache. In at least one embodiment,cache memory is shared among various components of processor 1102. In atleast one embodiment, processor 1102 also uses an external cache (e.g.,a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which maybe shared among processor cores 1107 using known cache coherencytechniques. In at least one embodiment, register file 1106 isadditionally included in processor 1102 which may include differenttypes of registers for storing different types of data (e.g., integerregisters, floating point registers, status registers, and aninstruction pointer register). In at least one embodiment, register file1106 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 1102 are coupledwith one or more interface bus(es) 1110 to transmit communicationsignals such as address, data, or control signals between processor 1102and other components in system 1100. In at least one embodiment,interface bus 1110, in one embodiment, can be a processor bus, such as aversion of a Direct Media Interface (DMI) bus. In at least oneembodiment, interface 1110 is not limited to a DMI bus, and may includeone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress), memory busses, or other types of interface busses. In at leastone embodiment processor(s) 1102 include an integrated memory controller1116 and a platform controller hub 1130. In at least one embodiment,memory controller 1116 facilitates communication between a memory deviceand other components of system 1100, while platform controller hub (PCH)1130 provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device 1120 can be a dynamic randomaccess memory (DRAM) device, a static random access memory (SRAM)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as process memory. Inat least one embodiment memory device 1120 can operate as system memoryfor system 1100, to store data 1122 and instructions 1121 for use whenone or more processors 1102 executes an application or process. In atleast one embodiment, memory controller 1116 also couples with anoptional external graphics processor 1112, which may communicate withone or more graphics processors 1108 in processors 1102 to performgraphics and media operations. In at least one embodiment, a displaydevice 1111 can connect to processor(s) 1102. In at least one embodimentdisplay device 1111 can include one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 1111 caninclude a head mounted display (HMD) such as a stereoscopic displaydevice for use in virtual reality (VR) applications or augmented reality(AR) applications.

In at least one embodiment, platform controller hub 1130 enablesperipherals to connect to memory device 1120 and processor 1102 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 1146, a network controller1134, a firmware interface 1128, a wireless transceiver 1126, touchsensors 1125, a data storage device 1124 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 1124 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). In at least one embodiment, touch sensors 1125 can includetouch screen sensors, pressure sensors, or fingerprint sensors. In atleast one embodiment, wireless transceiver 1126 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at leastone embodiment, firmware interface 1128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). In at least one embodiment, network controller 1134can enable a network connection to a wired network. In at least oneembodiment, a high-performance network controller (not shown) coupleswith interface bus 1110. In at least one embodiment, audio controller1146 is a multi-channel high definition audio controller. In at leastone embodiment, system 1100 includes an optional legacy I/O controller1140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices tosystem. In at least one embodiment, platform controller hub 1130 canalso connect to one or more Universal Serial Bus (USB) controllers 1142connect input devices, such as keyboard and mouse 1143 combinations, acamera 1144, or other USB input devices.

In at least one embodiment, an instance of memory controller 1116 andplatform controller hub 1130 may be integrated into a discreet externalgraphics processor, such as external graphics processor 1112. In atleast one embodiment, platform controller hub 1130 and/or memorycontroller 1116 may be external to one or more processor(s) 1102. Forexample, in at least one embodiment, system 1100 can include an externalmemory controller 1116 and platform controller hub 1130, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 1102.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7A and/or 7B. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into graphics processor 1500. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in a graphics processor. Moreover, inat least one embodiment, inferencing and/or training operationsdescribed herein may be done using logic other than logic illustrated inFIG. 7A or 7B. In at least one embodiment, weight parameters may bestored in on-chip or off-chip memory and/or registers (shown or notshown) that configure ALUs of a graphics processor to perform one ormore machine learning algorithms, neural network architectures, usecases, or training techniques described herein.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

FIG. 12 is a block diagram of a processor 1200 having one or moreprocessor cores 1202A-1202N, an integrated memory controller 1214, andan integrated graphics processor 1208, according to at least oneembodiment. In at least one embodiment, processor 1200 can includeadditional cores up to and including additional core 1202N representedby dashed lined boxes. In at least one embodiment, each of processorcores 1202A-1202N includes one or more internal cache units 1204A-1204N.In at least one embodiment, each processor core also has access to oneor more shared cached units 1206.

In at least one embodiment, internal cache units 1204A-1204N and sharedcache units 1206 represent a cache memory hierarchy within processor1200. In at least one embodiment, cache memory units 1204A-1204N mayinclude at least one level of instruction and data cache within eachprocessor core and one or more levels of shared mid-level cache, such asa Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache,where a highest level of cache before external memory is classified asan LLC. In at least one embodiment, cache coherency logic maintainscoherency between various cache units 1206 and 1204A-1204N.

In at least one embodiment, processor 1200 may also include a set of oneor more bus controller units 1216 and a system agent core 1210. In atleast one embodiment, one or more bus controller units 1216 manage a setof peripheral buses, such as one or more PCI or PCI express busses. Inat least one embodiment, system agent core 1210 provides managementfunctionality for various processor components. In at least oneembodiment, system agent core 1210 includes one or more integratedmemory controllers 1214 to manage access to various external memorydevices (not shown).

In at least one embodiment, one or more of processor cores 1202A-1202Ninclude support for simultaneous multi-threading. In at least oneembodiment, system agent core 1210 includes components for coordinatingand operating cores 1202A-1202N during multi-threaded processing. In atleast one embodiment, system agent core 1210 may additionally include apower control unit (PCU), which includes logic and components toregulate one or more power states of processor cores 1202A-1202N andgraphics processor 1208.

In at least one embodiment, processor 1200 additionally includesgraphics processor 1208 to execute graphics processing operations. In atleast one embodiment, graphics processor 1208 couples with shared cacheunits 1206, and system agent core 1210, including one or more integratedmemory controllers 1214. In at least one embodiment, system agent core1210 also includes a display controller 1211 to drive graphics processoroutput to one or more coupled displays. In at least one embodiment,display controller 1211 may also be a separate module coupled withgraphics processor 1208 via at least one interconnect, or may beintegrated within graphics processor 1208.

In at least one embodiment, a ring based interconnect unit 1212 is usedto couple internal components of processor 1200. In at least oneembodiment, an alternative interconnect unit may be used, such as apoint-to-point interconnect, a switched interconnect, or othertechniques. In at least one embodiment, graphics processor 1208 coupleswith ring interconnect 1212 via an I/O link 1213.

In at least one embodiment, I/O link 1213 represents at least one ofmultiple varieties of I/O interconnects, including an on package I/Ointerconnect which facilitates communication between various processorcomponents and a high-performance embedded memory module 1218, such asan eDRAM module. In at least one embodiment, each of processor cores1202A-1202N and graphics processor 1208 use embedded memory modules 1218as a shared Last Level Cache.

In at least one embodiment, processor cores 1202A-1202N are homogenouscores executing a common instruction set architecture. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofinstruction set architecture (ISA), where one or more of processor cores1202A-1202N execute a common instruction set, while one or more othercores of processor cores 1202A-1202N executes a subset of a commoninstruction set or a different instruction set. In at least oneembodiment, processor cores 1202A-1202N are heterogeneous in terms ofmicroarchitecture, where one or more cores having a relatively higherpower consumption couple with one or more power cores having a lowerpower consumption. In at least one embodiment, processor 1200 can beimplemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic 715 are used to perform inferencingand/or training operations associated with one or more embodiments.Details regarding inference and/or training logic 715 are provided belowin conjunction with FIGS. 7a and/or 7 b. In at least one embodimentportions or all of inference and/or training logic 715 may beincorporated into processor 1200. For example, in at least oneembodiment, training and/or inferencing techniques described herein mayuse one or more of ALUs embodied in graphics processor 1512, graphicscore(s) 1202A-1202N, or other components in FIG. 12. Moreover, in atleast one embodiment, inferencing and/or training operations describedherein may be done using logic other than logic illustrated in FIG. 7Aor 7B. In at least one embodiment, weight parameters may be stored inon-chip or off-chip memory and/or registers (shown or not shown) thatconfigure ALUs of graphics processor 1200 to perform one or more machinelearning algorithms, neural network architectures, use cases, ortraining techniques described herein.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

Virtualized Computing Platform

FIG. 13 is an example data flow diagram for a process 1300 of generatingand deploying an image processing and inferencing pipeline, inaccordance with at least one embodiment. In at least one embodiment,process 1300 may be deployed for use with imaging devices, processingdevices, and/or other device types at one or more facilities 1302.Process 1300 may be executed within a training system 1304 and/or adeployment system 1306. In at least one embodiment, training system 1304may be used to perform training, deployment, and implementation ofmachine learning models (e.g., neural networks, object detectionalgorithms, computer vision algorithms, etc.) for use in deploymentsystem 1306. In at least one embodiment, deployment system 1306 may beconfigured to offload processing and compute resources among adistributed computing environment to reduce infrastructure requirementsat facility 1302. In at least one embodiment, one or more applicationsin a pipeline may use or call upon services (e.g., inference,visualization, compute, AI, etc.) of deployment system 1306 duringexecution of applications.

In at least one embodiment, some of applications used in advancedprocessing and inferencing pipelines may use machine learning models orother AI to perform one or more processing steps. In at least oneembodiment, machine learning models may be trained at facility 1302using data 1308 (such as imaging data) generated at facility 1302 (andstored on one or more picture archiving and communication system (PACS)servers at facility 1302), may be trained using imaging or sequencingdata 1308 from another facility(ies), or a combination thereof. In atleast one embodiment, training system 1304 may be used to provideapplications, services, and/or other resources for generating working,deployable machine learning models for deployment system 1306.

In at least one embodiment, model registry 1324 may be backed by objectstorage that may support versioning and object metadata. In at least oneembodiment, object storage may be accessible through, for example, acloud storage (e.g., cloud 1426 of FIG. 14) compatible applicationprogramming interface (API) from within a cloud platform. In at leastone embodiment, machine learning models within model registry 1324 mayuploaded, listed, modified, or deleted by developers or partners of asystem interacting with an API. In at least one embodiment, an API mayprovide access to methods that allow users with appropriate credentialsto associate models with applications, such that models may be executedas part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline 1404 (FIG. 14) may includea scenario where facility 1302 is training their own machine learningmodel, or has an existing machine learning model that needs to beoptimized or updated. In at least one embodiment, imaging data 1308generated by imaging device(s), sequencing devices, and/or other devicetypes may be received. In at least one embodiment, once imaging data1308 is received, AI-assisted annotation 1310 may be used to aid ingenerating annotations corresponding to imaging data 1308 to be used asground truth data for a machine learning model. In at least oneembodiment, AI-assisted annotation 1310 may include one or more machinelearning models (e.g., convolutional neural networks (CNNs)) that may betrained to generate annotations corresponding to certain types ofimaging data 1308 (e.g., from certain devices). In at least oneembodiment, AI-assisted annotations 1310 may then be used directly, ormay be adjusted or fine-tuned using an annotation tool to generateground truth data. In at least one embodiment, AI-assisted annotations1310, labeled clinic data 1312, or a combination thereof may be used asground truth data for training a machine learning model. In at least oneembodiment, a trained machine learning model may be referred to asoutput model 1316, and may be used by deployment system 1306, asdescribed herein.

In at least one embodiment, training pipeline 1404 (FIG. 14) may includea scenario where facility 1302 needs a machine learning model for use inperforming one or more processing tasks for one or more applications indeployment system 1306, but facility 1302 may not currently have such amachine learning model (or may not have a model that is optimized,efficient, or effective for such purposes). In at least one embodiment,an existing machine learning model may be selected from a model registry1324. In at least one embodiment, model registry 1324 may includemachine learning models trained to perform a variety of differentinference tasks on imaging data. In at least one embodiment, machinelearning models in model registry 1324 may have been trained on imagingdata from different facilities than facility 1302 (e.g., facilitiesremotely located). In at least one embodiment, machine learning modelsmay have been trained on imaging data from one location, two locations,or any number of locations. In at least one embodiment, when beingtrained on imaging data from a specific location, training may takeplace at that location, or at least in a manner that protectsconfidentiality of imaging data or restricts imaging data from beingtransferred off-premises. In at least one embodiment, once a model istrained—or partially trained—at one location, a machine learning modelmay be added to model registry 1324. In at least one embodiment, amachine learning model may then be retrained, or updated, at any numberof other facilities, and a retrained or updated model may be madeavailable in model registry 1324. In at least one embodiment, a machinelearning model may then be selected from model registry 1324—andreferred to as output model 1316—and may be used in deployment system1306 to perform one or more processing tasks for one or moreapplications of a deployment system.

In at least one embodiment, training pipeline 1404 (FIG. 14), a scenariomay include facility 1302 requiring a machine learning model for use inperforming one or more processing tasks for one or more applications indeployment system 1306, but facility 1302 may not currently have such amachine learning model (or may not have a model that is optimized,efficient, or effective for such purposes). In at least one embodiment,a machine learning model selected from model registry 1324 may not befine-tuned or optimized for imaging data 1308 generated at facility 1302because of differences in populations, robustness of training data usedto train a machine learning model, diversity in anomalies of trainingdata, and/or other issues with training data. In at least oneembodiment, AI-assisted annotation 1310 may be used to aid in generatingannotations corresponding to imaging data 1308 to be used as groundtruth data for retraining or updating a machine learning model. In atleast one embodiment, labeled data 1312 may be used as ground truth datafor training a machine learning model. In at least one embodiment,retraining or updating a machine learning model may be referred to asmodel training 1314. In at least one embodiment, model training1314—e.g., AI-assisted annotations 1310, labeled clinic data 1312, or acombination thereof—may be used as ground truth data for retraining orupdating a machine learning model. In at least one embodiment, a trainedmachine learning model may be referred to as output model 1316, and maybe used by deployment system 1306, as described herein.

In at least one embodiment, deployment system 1306 may include software1318, services 1320, hardware 1322, and/or other components, features,and functionality. In at least one embodiment, deployment system 1306may include a software “stack,” such that software 1318 may be built ontop of services 1320 and may use services 1320 to perform some or all ofprocessing tasks, and services 1320 and software 1318 may be built ontop of hardware 1322 and use hardware 1322 to execute processing,storage, and/or other compute tasks of deployment system 1306. In atleast one embodiment, software 1318 may include any number of differentcontainers, where each container may execute an instantiation of anapplication. In at least one embodiment, each application may performone or more processing tasks in an advanced processing and inferencingpipeline (e.g., inferencing, object detection, feature detection,segmentation, image enhancement, calibration, etc.). In at least oneembodiment, an advanced processing and inferencing pipeline may bedefined based on selections of different containers that are desired orrequired for processing imaging data 1308, in addition to containersthat receive and configure imaging data for use by each container and/orfor use by facility 1302 after processing through a pipeline (e.g., toconvert outputs back to a usable data type). In at least one embodiment,a combination of containers within software 1318 (e.g., that make up apipeline) may be referred to as a virtual instrument (as described inmore detail herein), and a virtual instrument may leverage services 1320and hardware 1322 to execute some or all processing tasks ofapplications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive inputdata (e.g., imaging data 1308) in a specific format in response to aninference request (e.g., a request from a user of deployment system1306). In at least one embodiment, input data may be representative ofone or more images, video, and/or other data representations generatedby one or more imaging devices. In at least one embodiment, data mayundergo pre-processing as part of data processing pipeline to preparedata for processing by one or more applications. In at least oneembodiment, post-processing may be performed on an output of one or moreinferencing tasks or other processing tasks of a pipeline to prepare anoutput data for a next application and/or to prepare output data fortransmission and/or use by a user (e.g., as a response to an inferencerequest). In at least one embodiment, inferencing tasks may be performedby one or more machine learning models, such as trained or deployedneural networks, which may include output models 1316 of training system1304.

In at least one embodiment, tasks of data processing pipeline may beencapsulated in a container(s) that each represents a discrete, fullyfunctional instantiation of an application and virtualized computingenvironment that is able to reference machine learning models. In atleast one embodiment, containers or applications may be published into aprivate (e.g., limited access) area of a container registry (describedin more detail herein), and trained or deployed models may be stored inmodel registry 1324 and associated with one or more applications. In atleast one embodiment, images of applications (e.g., container images)may be available in a container registry, and once selected by a userfrom a container registry for deployment in a pipeline, an image may beused to generate a container for an instantiation of an application foruse by a user's system.

In at least one embodiment, developers (e.g., software developers,clinicians, doctors, etc.) may develop, publish, and store applications(e.g., as containers) for performing image processing and/or inferencingon supplied data. In at least one embodiment, development, publishing,and/or storing may be performed using a software development kit (SDK)associated with a system (e.g., to ensure that an application and/orcontainer developed is compliant with or compatible with a system). Inat least one embodiment, an application that is developed may be testedlocally (e.g., at a first facility, on data from a first facility) withan SDK which may support at least some of services 1320 as a system(e.g., system 1400 of FIG. 14). In at least one embodiment, becauseDICOM objects may contain anywhere from one to hundreds of images orother data types, and due to a variation in data, a developer may beresponsible for managing (e.g., setting constructs for, buildingpre-processing into an application, etc.) extraction and preparation ofincoming data. In at least one embodiment, once validated by system 1400(e.g., for accuracy), an application may be available in a containerregistry for selection and/or implementation by a user to perform one ormore processing tasks with respect to data at a facility (e.g., a secondfacility) of a user.

In at least one embodiment, developers may then share applications orcontainers through a network for access and use by users of a system(e.g., system 1400 of FIG. 14). In at least one embodiment, completedand validated applications or containers may be stored in a containerregistry and associated machine learning models may be stored in modelregistry 1324. In at least one embodiment, a requesting entity—whoprovides an inference or image processing request—may browse a containerregistry and/or model registry 1324 for an application, container,dataset, machine learning model, etc., select a desired combination ofelements for inclusion in data processing pipeline, and submit animaging processing request. In at least one embodiment, a request mayinclude input data (and associated patient data, in some examples) thatis necessary to perform a request, and/or may include a selection ofapplication(s) and/or machine learning models to be executed inprocessing a request. In at least one embodiment, a request may then bepassed to one or more components of deployment system 1306 (e.g., acloud) to perform processing of data processing pipeline. In at leastone embodiment, processing by deployment system 1306 may includereferencing selected elements (e.g., applications, containers, models,etc.) from a container registry and/or model registry 1324. In at leastone embodiment, once results are generated by a pipeline, results may bereturned to a user for reference (e.g., for viewing in a viewingapplication suite executing on a local, on-premises workstation orterminal).

In at least one embodiment, to aid in processing or execution ofapplications or containers in pipelines, services 1320 may be leveraged.In at least one embodiment, services 1320 may include compute services,artificial intelligence (AI) services, visualization services, and/orother service types. In at least one embodiment, services 1320 mayprovide functionality that is common to one or more applications insoftware 1318, so functionality may be abstracted to a service that maybe called upon or leveraged by applications. In at least one embodiment,functionality provided by services 1320 may run dynamically and moreefficiently, while also scaling well by allowing applications to processdata in parallel (e.g., using a parallel computing platform 1430 (FIG.14)). In at least one embodiment, rather than each application thatshares a same functionality offered by a service 1320 being required tohave a respective instance of service 1320, service 1320 may be sharedbetween and among various applications. In at least one embodiment,services may include an inference server or engine that may be used forexecuting detection or segmentation tasks, as non-limiting examples. Inat least one embodiment, a model training service may be included thatmay provide machine learning model training and/or retrainingcapabilities. In at least one embodiment, a data augmentation servicemay further be included that may provide GPU accelerated data (e.g.,DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing,scaling, and/or other augmentation. In at least one embodiment, avisualization service may be used that may add image renderingeffects—such as ray-tracing, rasterization, denoising, sharpening,etc.—to add realism to two-dimensional (2D) and/or three-dimensional(3D) models. In at least one embodiment, virtual instrument services maybe included that provide for beam-forming, segmentation, inferencing,imaging, and/or support for other applications within pipelines ofvirtual instruments.

In at least one embodiment, where a service 1320 includes an AI service(e.g., an inference service), one or more machine learning models may beexecuted by calling upon (e.g., as an API call) an inference service(e.g., an inference server) to execute machine learning model(s), orprocessing thereof, as part of application execution. In at least oneembodiment, where another application includes one or more machinelearning models for segmentation tasks, an application may call upon aninference service to execute machine learning models for performing oneor more of processing operations associated with segmentation tasks. Inat least one embodiment, software 1318 implementing advanced processingand inferencing pipeline that includes segmentation application andanomaly detection application may be streamlined because eachapplication may call upon a same inference service to perform one ormore inferencing tasks.

In at least one embodiment, hardware 1322 may include GPUs, CPUs,graphics cards, an AI/deep learning system (e.g., an AI supercomputer,such as NVIDIA's DGX), a cloud platform, or a combination thereof. In atleast one embodiment, different types of hardware 1322 may be used toprovide efficient, purpose-built support for software 1318 and services1320 in deployment system 1306. In at least one embodiment, use of GPUprocessing may be implemented for processing locally (e.g., at facility1302), within an AI/deep learning system, in a cloud system, and/or inother processing components of deployment system 1306 to improveefficiency, accuracy, and efficacy of image processing and generation.In at least one embodiment, software 1318 and/or services 1320 may beoptimized for GPU processing with respect to deep learning, machinelearning, and/or high-performance computing, as non-limiting examples.In at least one embodiment, at least some of computing environment ofdeployment system 1306 and/or training system 1304 may be executed in adatacenter one or more supercomputers or high performance computingsystems, with GPU optimized software (e.g., hardware and softwarecombination of NVIDIA's DGX System). In at least one embodiment,hardware 1322 may include any number of GPUs that may be called upon toperform processing of data in parallel, as described herein. In at leastone embodiment, cloud platform may further include GPU processing forGPU-optimized execution of deep learning tasks, machine learning tasks,or other computing tasks. In at least one embodiment, cloud platform(e.g., NVIDIA's NGC) may be executed using an AI/deep learningsupercomputer(s) and/or GPU-optimized software (e.g., as provided onNVIDIA's DGX Systems) as a hardware abstraction and scaling platform. Inat least one embodiment, cloud platform may integrate an applicationcontainer clustering system or orchestration system (e.g., KUBERNETES)on multiple GPUs to enable seamless scaling and load balancing.

FIG. 14 is a system diagram for an example system 1400 for generatingand deploying an imaging deployment pipeline, in accordance with atleast one embodiment. In at least one embodiment, system 1400 may beused to implement process 1300 of FIG. 13 and/or other processesincluding advanced processing and inferencing pipelines. In at least oneembodiment, system 1400 may include training system 1304 and deploymentsystem 1306. In at least one embodiment, training system 1304 anddeployment system 1306 may be implemented using software 1318, services1320, and/or hardware 1322, as described herein.

In at least one embodiment, system 1400 (e.g., training system 1304and/or deployment system 1306) may implemented in a cloud computingenvironment (e.g., using cloud 1426). In at least one embodiment, system1400 may be implemented locally with respect to a healthcare servicesfacility, or as a combination of both cloud and local computingresources. In at least one embodiment, access to APIs in cloud 1426 maybe restricted to authorized users through enacted security measures orprotocols. In at least one embodiment, a security protocol may includeweb tokens that may be signed by an authentication (e.g., AuthN, AuthZ,Gluecon, etc.) service and may carry appropriate authorization. In atleast one embodiment, APIs of virtual instruments (described herein), orother instantiations of system 1400, may be restricted to a set ofpublic IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system 1400 maycommunicate between and among one another using any of a variety ofdifferent network types, including but not limited to local areanetworks (LANs) and/or wide area networks (WANs) via wired and/orwireless communication protocols. In at least one embodiment,communication between facilities and components of system 1400 (e.g.,for transmitting inference requests, for receiving results of inferencerequests, etc.) may be communicated over data bus(ses), wireless dataprotocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system 1304 may execute trainingpipelines 1404, similar to those described herein with respect to FIG.13. In at least one embodiment, where one or more machine learningmodels are to be used in deployment pipelines 1410 by deployment system1306, training pipelines 1404 may be used to train or retrain one ormore (e.g. pre-trained) models, and/or implement one or more ofpre-trained models 1406 (e.g., without a need for retraining orupdating). In at least one embodiment, as a result of training pipelines1404, output model(s) 1316 may be generated. In at least one embodiment,training pipelines 1404 may include any number of processing steps, suchas but not limited to imaging data (or other input data) conversion oradaption In at least one embodiment, for different machine learningmodels used by deployment system 1306, different training pipelines 1404may be used. In at least one embodiment, training pipeline 1404 similarto a first example described with respect to FIG. 13 may be used for afirst machine learning model, training pipeline 1404 similar to a secondexample described with respect to FIG. 13 may be used for a secondmachine learning model, and training pipeline 1404 similar to a thirdexample described with respect to FIG. 13 may be used for a thirdmachine learning model. In at least one embodiment, any combination oftasks within training system 1304 may be used depending on what isrequired for each respective machine learning model. In at least oneembodiment, one or more of machine learning models may already betrained and ready for deployment so machine learning models may notundergo any processing by training system 1304, and may be implementedby deployment system 1306.

In at least one embodiment, output model(s) 1316 and/or pre-trainedmodel(s) 1406 may include any types of machine learning models dependingon implementation or embodiment. In at least one embodiment, and withoutlimitation, machine learning models used by system 1400 may includemachine learning model(s) using linear regression, logistic regression,decision trees, support vector machines (SVM), Naïve Bayes, k-nearestneighbor (Knn), K means clustering, random forest, dimensionalityreduction algorithms, gradient boosting algorithms, neural networks(e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/ShortTerm Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In at least one embodiment, training pipelines 1404 may includeAI-assisted annotation, as described in more detail herein with respectto at least FIG. 15B. In at least one embodiment, labeled data 1312(e.g., traditional annotation) may be generated by any number oftechniques. In at least one embodiment, labels or other annotations maybe generated within a drawing program (e.g., an annotation program), acomputer aided design (CAD) program, a labeling program, another type ofprogram suitable for generating annotations or labels for ground truth,and/or may be hand drawn, in some examples. In at least one embodiment,ground truth data may be synthetically produced (e.g., generated fromcomputer models or renderings), real produced (e.g., designed andproduced from real-world data), machine-automated (e.g., using featureanalysis and learning to extract features from data and then generatelabels), human annotated (e.g., labeler, or annotation expert, defineslocation of labels), and/or a combination thereof. In at least oneembodiment, for each instance of imaging data 1308 (or other data typeused by machine learning models), there may be corresponding groundtruth data generated by training system 1304. In at least oneembodiment, AI-assisted annotation may be performed as part ofdeployment pipelines 1410; either in addition to, or in lieu ofAI-assisted annotation included in training pipelines 1404. In at leastone embodiment, system 1400 may include a multi-layer platform that mayinclude a software layer (e.g., software 1318) of diagnosticapplications (or other application types) that may perform one or moremedical imaging and diagnostic functions. In at least one embodiment,system 1400 may be communicatively coupled to (e.g., via encryptedlinks) PACS server networks of one or more facilities. In at least oneembodiment, system 1400 may be configured to access and referenced datafrom PACS servers to perform operations, such as training machinelearning models, deploying machine learning models, image processing,inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as asecure, encrypted, and/or authenticated API through which applicationsor containers may be invoked (e.g., called) from an externalenvironment(s) (e.g., facility 1302). In at least one embodiment,applications may then call or execute one or more services 1320 forperforming compute, AI, or visualization tasks associated withrespective applications, and software 1318 and/or services 1320 mayleverage hardware 1322 to perform processing tasks in an effective andefficient manner.

In at least one embodiment, deployment system 1306 may executedeployment pipelines 1410. In at least one embodiment, deploymentpipelines 1410 may include any number of applications that may besequentially, non-sequentially, or otherwise applied to imaging data(and/or other data types) generated by imaging devices, sequencingdevices, genomics devices, etc.—including AI-assisted annotation, asdescribed above. In at least one embodiment, as described herein, adeployment pipeline 1410 for an individual device may be referred to asa virtual instrument for a device (e.g., a virtual ultrasoundinstrument, a virtual CT scan instrument, a virtual sequencinginstrument, etc.). In at least one embodiment, for a single device,there may be more than one deployment pipeline 1410 depending oninformation desired from data generated by a device. In at least oneembodiment, where detections of anomalies are desired from an Millmachine, there may be a first deployment pipeline 1410, and where imageenhancement is desired from output of an Mill machine, there may be asecond deployment pipeline 1410.

In at least one embodiment, an image generation application may includea processing task that includes use of a machine learning model. In atleast one embodiment, a user may desire to use their own machinelearning model, or to select a machine learning model from modelregistry 1324. In at least one embodiment, a user may implement theirown machine learning model or select a machine learning model forinclusion in an application for performing a processing task. In atleast one embodiment, applications may be selectable and customizable,and by defining constructs of applications, deployment andimplementation of applications for a particular user are presented as amore seamless user experience. In at least one embodiment, by leveragingother features of system 1400—such as services 1320 and hardware1322—deployment pipelines 1410 may be even more user friendly, providefor easier integration, and produce more accurate, efficient, and timelyresults.

In at least one embodiment, deployment system 1306 may include a userinterface 1414 (e.g., a graphical user interface, a web interface, etc.)that may be used to select applications for inclusion in deploymentpipeline(s) 1410, arrange applications, modify or change applications orparameters or constructs thereof, use and interact with deploymentpipeline(s) 1410 during set-up and/or deployment, and/or to otherwiseinteract with deployment system 1306. In at least one embodiment,although not illustrated with respect to training system 1304, userinterface 1414 (or a different user interface) may be used for selectingmodels for use in deployment system 1306, for selecting models fortraining, or retraining, in training system 1304, and/or for otherwiseinteracting with training system 1304.

In at least one embodiment, pipeline manager 1412 may be used, inaddition to an application orchestration system 1428, to manageinteraction between applications or containers of deployment pipeline(s)1410 and services 1320 and/or hardware 1322. In at least one embodiment,pipeline manager 1412 may be configured to facilitate interactions fromapplication to application, from application to service 1320, and/orfrom application or service to hardware 1322. In at least oneembodiment, although illustrated as included in software 1318, this isnot intended to be limiting, and in some examples (e.g., as illustratedin FIG. 12 cc) pipeline manager 1412 may be included in services 1320.In at least one embodiment, application orchestration system 1428 (e.g.,Kubernetes, DOCKER, etc.) may include a container orchestration systemthat may group applications into containers as logical units forcoordination, management, scaling, and deployment. In at least oneembodiment, by associating applications from deployment pipeline(s) 1410(e.g., a reconstruction application, a segmentation application, etc.)with individual containers, each application may execute in aself-contained environment (e.g., at a kernel level) to increase speedand efficiency.

In at least one embodiment, each application and/or container (or imagethereof) may be individually developed, modified, and deployed (e.g., afirst user or developer may develop, modify, and deploy a firstapplication and a second user or developer may develop, modify, anddeploy a second application separate from a first user or developer),which may allow for focus on, and attention to, a task of a singleapplication and/or container(s) without being hindered by tasks ofanother application(s) or container(s). In at least one embodiment,communication, and cooperation between different containers orapplications may be aided by pipeline manager 1412 and applicationorchestration system 1428. In at least one embodiment, so long as anexpected input and/or output of each container or application is knownby a system (e.g., based on constructs of applications or containers),application orchestration system 1428 and/or pipeline manager 1412 mayfacilitate communication among and between, and sharing of resourcesamong and between, each of applications or containers. In at least oneembodiment, because one or more of applications or containers indeployment pipeline(s) 1410 may share same services and resources,application orchestration system 1428 may orchestrate, load balance, anddetermine sharing of services or resources between and among variousapplications or containers. In at least one embodiment, a scheduler maybe used to track resource requirements of applications or containers,current usage or planned usage of these resources, and resourceavailability. In at least one embodiment, a scheduler may thus allocateresources to different applications and distribute resources between andamong applications in view of requirements and availability of a system.In some examples, a scheduler (and/or other component of applicationorchestration system 1428) may determine resource availability anddistribution based on constraints imposed on a system (e.g., userconstraints), such as quality of service (QoS), urgency of need for dataoutputs (e.g., to determine whether to execute real-time processing ordelayed processing), etc.

In at least one embodiment, services 1320 leveraged by and shared byapplications or containers in deployment system 1306 may include computeservices 1416, AI services 1418, visualization services 1420, and/orother service types. In at least one embodiment, applications may call(e.g., execute) one or more of services 1320 to perform processingoperations for an application. In at least one embodiment, computeservices 1416 may be leveraged by applications to performsuper-computing or other high-performance computing (HPC) tasks. In atleast one embodiment, compute service(s) 1416 may be leveraged toperform parallel processing (e.g., using a parallel computing platform1430) for processing data through one or more of applications and/or oneor more tasks of a single application, substantially simultaneously. Inat least one embodiment, parallel computing platform 1430 (e.g.,NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU)(e.g., GPUs 1422). In at least one embodiment, a software layer ofparallel computing platform 1430 may provide access to virtualinstruction sets and parallel computational elements of GPUs, forexecution of compute kernels. In at least one embodiment, parallelcomputing platform 1430 may include memory and, in some embodiments, amemory may be shared between and among multiple containers, and/orbetween and among different processing tasks within a single container.In at least one embodiment, inter-process communication (IPC) calls maybe generated for multiple containers and/or for multiple processeswithin a container to use same data from a shared segment of memory ofparallel computing platform 1430 (e.g., where multiple different stagesof an application or multiple applications are processing sameinformation). In at least one embodiment, rather than making a copy ofdata and moving data to different locations in memory (e.g., aread/write operation), same data in same location of a memory may beused for any number of processing tasks (e.g., at a same time, atdifferent times, etc.). In at least one embodiment, as data is used togenerate new data as a result of processing, this information of a newlocation of data may be stored and shared between various applications.In at least one embodiment, location of data and a location of updatedor modified data may be part of a definition of how a payload isunderstood within containers.

In at least one embodiment, AI services 1418 may be leveraged to performinferencing services for executing machine learning model(s) associatedwith applications (e.g., tasked with performing one or more processingtasks of an application). In at least one embodiment, AI services 1418may leverage AI system 1424 to execute machine learning model(s) (e.g.,neural networks, such as CNNs) for segmentation, reconstruction, objectdetection, feature detection, classification, and/or other inferencingtasks. In at least one embodiment, applications of deploymentpipeline(s) 1410 may use one or more of output models 1316 from trainingsystem 1304 and/or other models of applications to perform inference onimaging data. In at least one embodiment, two or more examples ofinferencing using application orchestration system 1428 (e.g., ascheduler) may be available. In at least one embodiment, a firstcategory may include a high priority/low latency path that may achievehigher service level agreements, such as for performing inference onurgent requests during an emergency, or for a radiologist duringdiagnosis. In at least one embodiment, a second category may include astandard priority path that may be used for requests that may benon-urgent or where analysis may be performed at a later time. In atleast one embodiment, application orchestration system 1428 maydistribute resources (e.g., services 1320 and/or hardware 1322) based onpriority paths for different inferencing tasks of AI services 1418.

In at least one embodiment, shared storage may be mounted to AI services1418 within system 1400. In at least one embodiment, shared storage mayoperate as a cache (or other storage device type) and may be used toprocess inference requests from applications. In at least oneembodiment, when an inference request is submitted, a request may bereceived by a set of API instances of deployment system 1306, and one ormore instances may be selected (e.g., for best fit, for load balancing,etc.) to process a request. In at least one embodiment, to process arequest, a request may be entered into a database, a machine learningmodel may be located from model registry 1324 if not already in a cache,a validation step may ensure appropriate machine learning model isloaded into a cache (e.g., shared storage), and/or a copy of a model maybe saved to a cache. In at least one embodiment, a scheduler (e.g., ofpipeline manager 1412) may be used to launch an application that isreferenced in a request if an application is not already running or ifthere are not enough instances of an application. In at least oneembodiment, if an inference server is not already launched to execute amodel, an inference server may be launched. Any number of inferenceservers may be launched per model. In at least one embodiment, in a pullmodel, in which inference servers are clustered, models may be cachedwhenever load balancing is advantageous. In at least one embodiment,inference servers may be statically loaded in corresponding, distributedservers.

In at least one embodiment, inferencing may be performed using aninference server that runs in a container. In at least one embodiment,an instance of an inference server may be associated with a model (andoptionally a plurality of versions of a model). In at least oneembodiment, if an instance of an inference server does not exist when arequest to perform inference on a model is received, a new instance maybe loaded. In at least one embodiment, when starting an inferenceserver, a model may be passed to an inference server such that a samecontainer may be used to serve different models so long as inferenceserver is running as a different instance.

In at least one embodiment, during application execution, an inferencerequest for a given application may be received, and a container (e.g.,hosting an instance of an inference server) may be loaded (if notalready), and a start procedure may be called. In at least oneembodiment, pre-processing logic in a container may load, decode, and/orperform any additional pre-processing on incoming data (e.g., using aCPU(s) and/or GPU(s)). In at least one embodiment, once data is preparedfor inference, a container may perform inference as necessary on data.In at least one embodiment, this may include a single inference call onone image (e.g., a hand X-ray), or may require inference on hundreds ofimages (e.g., a chest CT). In at least one embodiment, an applicationmay summarize results before completing, which may include, withoutlimitation, a single confidence score, pixel level-segmentation,voxel-level segmentation, generating a visualization, or generating textto summarize findings. In at least one embodiment, different models orapplications may be assigned different priorities. For example, somemodels may have a real-time (TAT<1 min) priority while others may havelower priority (e.g., TAT<10 min). In at least one embodiment, modelexecution times may be measured from requesting institution or entityand may include partner network traversal time, as well as execution onan inference service.

In at least one embodiment, transfer of requests between services 1320and inference applications may be hidden behind a software developmentkit (SDK), and robust transport may be provide through a queue. In atleast one embodiment, a request will be placed in a queue via an API foran individual application/tenant ID combination and an SDK will pull arequest from a queue and give a request to an application. In at leastone embodiment, a name of a queue may be provided in an environment fromwhere an SDK will pick it up. In at least one embodiment, asynchronouscommunication through a queue may be useful as it may allow any instanceof an application to pick up work as it becomes available. Results maybe transferred back through a queue, to ensure no data is lost. In atleast one embodiment, queues may also provide an ability to segmentwork, as highest priority work may go to a queue with most instances ofan application connected to it, while lowest priority work may go to aqueue with a single instance connected to it that processes tasks in anorder received. In at least one embodiment, an application may run on aGPU-accelerated instance generated in cloud 1426, and an inferenceservice may perform inferencing on a GPU.

In at least one embodiment, visualization services 1420 may be leveragedto generate visualizations for viewing outputs of applications and/ordeployment pipeline(s) 1410. In at least one embodiment, GPUs 1422 maybe leveraged by visualization services 1420 to generate visualizations.In at least one embodiment, rendering effects, such as ray-tracing, maybe implemented by visualization services 1420 to generate higher qualityvisualizations. In at least one embodiment, visualizations may include,without limitation, 2D image renderings, 3D volume renderings, 3D volumereconstruction, 2D tomographic slices, virtual reality displays,augmented reality displays, etc. In at least one embodiment, virtualizedenvironments may be used to generate a virtual interactive display orenvironment (e.g., a virtual environment) for interaction by users of asystem (e.g., doctors, nurses, radiologists, etc.). In at least oneembodiment, visualization services 1420 may include an internalvisualizer, cinematics, and/or other rendering or image processingcapabilities or functionality (e.g., ray tracing, rasterization,internal optics, etc.).

In at least one embodiment, hardware 1322 may include GPUs 1422, AIsystem 1424, cloud 1426, and/or any other hardware used for executingtraining system 1304 and/or deployment system 1306. In at least oneembodiment, GPUs 1422 (e.g., NVIDIA's TESLA and/or QUADRO GPUs) mayinclude any number of GPUs that may be used for executing processingtasks of compute services 1416, AI services 1418, visualization services1420, other services, and/or any of features or functionality ofsoftware 1318. For example, with respect to AI services 1418, GPUs 1422may be used to perform pre-processing on imaging data (or other datatypes used by machine learning models), post-processing on outputs ofmachine learning models, and/or to perform inferencing (e.g., to executemachine learning models). In at least one embodiment, cloud 1426, AIsystem 1424, and/or other components of system 1400 may use GPUs 1422.In at least one embodiment, cloud 1426 may include a GPU-optimizedplatform for deep learning tasks. In at least one embodiment, AI system1424 may use GPUs, and cloud 1426—or at least a portion tasked with deeplearning or inferencing—may be executed using one or more AI systems1424. As such, although hardware 1322 is illustrated as discretecomponents, this is not intended to be limiting, and any components ofhardware 1322 may be combined with, or leveraged by, any othercomponents of hardware 1322.

In at least one embodiment, AI system 1424 may include a purpose-builtcomputing system (e.g., a super-computer or an HPC) configured forinferencing, deep learning, machine learning, and/or other artificialintelligence tasks. In at least one embodiment, AI system 1424 (e.g.,NVIDIA's DGX) may include GPU-optimized software (e.g., a softwarestack) that may be executed using a plurality of GPUs 1422, in additionto CPUs, RAM, storage, and/or other components, features, orfunctionality. In at least one embodiment, one or more AI systems 1424may be implemented in cloud 1426 (e.g., in a data center) for performingsome or all of AI-based processing tasks of system 1400.

In at least one embodiment, cloud 1426 may include a GPU-acceleratedinfrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimizedplatform for executing processing tasks of system 1400. In at least oneembodiment, cloud 1426 may include an AI system(s) 1424 for performingone or more of AI-based tasks of system 1400 (e.g., as a hardwareabstraction and scaling platform). In at least one embodiment, cloud1426 may integrate with application orchestration system 1428 leveragingmultiple GPUs to enable seamless scaling and load balancing between andamong applications and services 1320. In at least one embodiment, cloud1426 may tasked with executing at least some of services 1320 of system1400, including compute services 1416, AI services 1418, and/orvisualization services 1420, as described herein. In at least oneembodiment, cloud 1426 may perform small and large batch inference(e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallelcomputing API and platform 1430 (e.g., NVIDIA's CUDA), executeapplication orchestration system 1428 (e.g., KUBERNETES), provide agraphics rendering API and platform (e.g., for ray-tracing, 2D graphics,3D graphics, and/or other rendering techniques to produce higher qualitycinematics), and/or may provide other functionality for system 1400.

FIG. 15A illustrates a data flow diagram for a process 1500 to train,retrain, or update a machine learning model, in accordance with at leastone embodiment. In at least one embodiment, process 1500 may be executedusing, as a non-limiting example, system 1400 of FIG. 14. In at leastone embodiment, process 1500 may leverage services 1320 and/or hardware1322 of system 1400, as described herein. In at least one embodiment,refined models 1512 generated by process 1500 may be executed bydeployment system 1306 for one or more containerized applications indeployment pipelines 1410.

In at least one embodiment, model training 1314 may include retrainingor updating an initial model 1504 (e.g., a pre-trained model) using newtraining data (e.g., new input data, such as customer dataset 1506,and/or new ground truth data associated with input data). In at leastone embodiment, to retrain, or update, initial model 1504, output orloss layer(s) of initial model 1504 may be reset, or deleted, and/orreplaced with an updated or new output or loss layer(s). In at least oneembodiment, initial model 1504 may have previously fine-tuned parameters(e.g., weights and/or biases) that remain from prior training, sotraining or retraining 1314 may not take as long or require as muchprocessing as training a model from scratch. In at least one embodiment,during model training 1314, by having reset or replaced output or losslayer(s) of initial model 1504, parameters may be updated and re-tunedfor a new data set based on loss calculations associated with accuracyof output or loss layer(s) at generating predictions on new, customerdataset 1506 (e.g., image data 1308 of FIG. 13).

In at least one embodiment, pre-trained models 1406 may be stored in adata store, or registry (e.g., model registry 1324 of FIG. 13). In atleast one embodiment, pre-trained models 1406 may have been trained, atleast in part, at one or more facilities other than a facility executingprocess 1500. In at least one embodiment, to protect privacy and rightsof patients, subjects, or clients of different facilities, pre-trainedmodels 1406 may have been trained, on-premise, using customer or patientdata generated on-premise. In at least one embodiment, pre-trainedmodels 1406 may be trained using cloud 1426 and/or other hardware 1322,but confidential, privacy protected patient data may not be transferredto, used by, or accessible to any components of cloud 1426 (or other offpremise hardware). In at least one embodiment, where a pre-trained model1406 is trained at using patient data from more than one facility,pre-trained model 1406 may have been individually trained for eachfacility prior to being trained on patient or customer data from anotherfacility. In at least one embodiment, such as where a customer orpatient data has been released of privacy concerns (e.g., by waiver, forexperimental use, etc.), or where a customer or patient data is includedin a public data set, a customer or patient data from any number offacilities may be used to train pre-trained model 1406 on-premise and/oroff premise, such as in a datacenter or other cloud computinginfrastructure.

In at least one embodiment, when selecting applications for use indeployment pipelines 1410, a user may also select machine learningmodels to be used for specific applications. In at least one embodiment,a user may not have a model for use, so a user may select a pre-trainedmodel 1406 to use with an application. In at least one embodiment,pre-trained model 1406 may not be optimized for generating accurateresults on customer dataset 1506 of a facility of a user (e.g., based onpatient diversity, demographics, types of medical imaging devices used,etc.). In at least one embodiment, prior to deploying pre-trained model1406 into deployment pipeline 1410 for use with an application(s),pre-trained model 1406 may be updated, retrained, and/or fine-tuned foruse at a respective facility.

In at least one embodiment, a user may select pre-trained model 1406that is to be updated, retrained, and/or fine-tuned, and pre-trainedmodel 1406 may be referred to as initial model 1504 for training system1304 within process 1500. In at least one embodiment, customer dataset1506 (e.g., imaging data, genomics data, sequencing data, or other datatypes generated by devices at a facility) may be used to perform modeltraining 1314 (which may include, without limitation, transfer learning)on initial model 1504 to generate refined model 1512. In at least oneembodiment, ground truth data corresponding to customer dataset 1506 maybe generated by training system 1304. In at least one embodiment, groundtruth data may be generated, at least in part, by clinicians,scientists, doctors, practitioners, at a facility (e.g., as labeledclinic data 1312 of FIG. 13).

In at least one embodiment, AI-assisted annotation 1310 may be used insome examples to generate ground truth data. In at least one embodiment,AI-assisted annotation 1310 (e.g., implemented using an AI-assistedannotation SDK) may leverage machine learning models (e.g., neuralnetworks) to generate suggested or predicted ground truth data for acustomer dataset. In at least one embodiment, user 1510 may useannotation tools within a user interface (a graphical user interface(GUI)) on computing device 1508.

In at least one embodiment, user 1510 may interact with a GUI viacomputing device 1508 to edit or fine-tune (auto)annotations. In atleast one embodiment, a polygon editing feature may be used to movevertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset 1506 has associatedground truth data, ground truth data (e.g., from AI-assisted annotation,manual labeling, etc.) may be used by during model training 1314 togenerate refined model 1512. In at least one embodiment, customerdataset 1506 may be applied to initial model 1504 any number of times,and ground truth data may be used to update parameters of initial model1504 until an acceptable level of accuracy is attained for refined model1512. In at least one embodiment, once refined model 1512 is generated,refined model 1512 may be deployed within one or more deploymentpipelines 1410 at a facility for performing one or more processing taskswith respect to medical imaging data.

In at least one embodiment, refined model 1512 may be uploaded topre-trained models 1406 in model registry 1324 to be selected by anotherfacility. In at least one embodiment, his process may be completed atany number of facilities such that refined model 1512 may be furtherrefined on new datasets any number of times to generate a more universalmodel.

FIG. 15B is an example illustration of a client-server architecture 1532to enhance annotation tools with pre-trained annotation models, inaccordance with at least one embodiment. In at least one embodiment,AI-assisted annotation tools 1536 may be instantiated based on aclient-server architecture 1532. In at least one embodiment, annotationtools 1536 in imaging applications may aid radiologists, for example,identify organs and abnormalities. In at least one embodiment, imagingapplications may include software tools that help user 1510 to identify,as a non-limiting example, a few extreme points on a particular organ ofinterest in raw images 1534 (e.g., in a 3D MRI or CT scan) and receiveauto-annotated results for all 2D slices of a particular organ. In atleast one embodiment, results may be stored in a data store as trainingdata 1538 and used as (for example and without limitation) ground truthdata for training. In at least one embodiment, when computing device1508 sends extreme points for AI-assisted annotation 1310, a deeplearning model, for example, may receive this data as input and returninference results of a segmented organ or abnormality. In at least oneembodiment, pre-instantiated annotation tools, such as AI-AssistedAnnotation Tool 1536B in FIG. 15B, may be enhanced by making API calls(e.g., API Call 1544) to a server, such as an Annotation AssistantServer 1540 that may include a set of pre-trained models 1542 stored inan annotation model registry, for example. In at least one embodiment,an annotation model registry may store pre-trained models 1542 (e.g.,machine learning models, such as deep learning models) that arepre-trained to perform AI-assisted annotation on a particular organ orabnormality. These models may be further updated by using trainingpipelines 1404. In at least one embodiment, pre-installed annotationtools may be improved over time as new labeled clinic data 1312 isadded.

Such components can be used to generate diverse scene graphs from one ormore rule sets, which can be used to generate training data or imagecontent representing one or more scenes of a virtual environment.

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. Term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to, orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinrange, unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. Use of term “set” (e.g., “a set of items”) or “subset,” unlessotherwise noted or contradicted by context, is to be construed as anonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, term “subset” of acorresponding set does not necessarily denote a proper subset ofcorresponding set, but subset and corresponding set may be equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. For instance, in illustrative example of a set havingthree members, conjunctive phrases “at least one of A, B, and C” and “atleast one of A, B and C” refer to any of following sets: {A}, {B}, {C},{A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language isnot generally intended to imply that certain embodiments require atleast one of A, at least one of B, and at least one of C each to bepresent. In addition, unless otherwise noted or contradicted by context,term “plurality” indicates a state of being plural (e.g., “a pluralityof items” indicates multiple items). A plurality is at least two items,but can be more when so indicated either explicitly or by context.Further, unless stated otherwise or otherwise clear from context, phrase“based on” means “based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium, for example, in form of acomputer program comprising a plurality of instructions executable byone or more processors. In at least one embodiment, a computer-readablestorage medium is a non-transitory computer-readable storage medium thatexcludes transitory signals (e.g., a propagating transient electric orelectromagnetic transmission) but includes non-transitory data storagecircuitry (e.g., buffers, cache, and queues) within transceivers oftransitory signals. In at least one embodiment, code (e.g., executablecode or source code) is stored on a set of one or more non-transitorycomputer-readable storage media having stored thereon executableinstructions (or other memory to store executable instructions) that,when executed (i.e., as a result of being executed) by one or moreprocessors of a computer system, cause computer system to performoperations described herein. A set of non-transitory computer-readablestorage media, in at least one embodiment, comprises multiplenon-transitory computer-readable storage media and one or more ofindividual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by differentprocessors—for example, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate embodiments ofdisclosure and does not pose a limitation on scope of disclosure unlessotherwise claimed. No language in specification should be construed asindicating any non-claimed element as essential to practice ofdisclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in particularexamples, “connected” or “coupled” may be used to indicate that two ormore elements are in direct or indirect physical or electrical contactwith each other. “Coupled” may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting examples,“processor” may be a CPU or a GPU. A “computing platform” may compriseone or more processors. As used herein, “software” processes mayinclude, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.Terms “system” and “method” are used herein interchangeably insofar assystem may embody one or more methods and methods may be considered asystem.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. Obtaining, acquiring,receiving, or inputting analog and digital data can be accomplished in avariety of ways such as by receiving data as a parameter of a functioncall or a call to an application programming interface. In someimplementations, process of obtaining, acquiring, receiving, orinputting analog or digital data can be accomplished by transferringdata via a serial or parallel interface. In another implementation,process of obtaining, acquiring, receiving, or inputting analog ordigital data can be accomplished by transferring data via a computernetwork from providing entity to acquiring entity. References may alsobe made to providing, outputting, transmitting, sending, or presentinganalog or digital data. In various examples, process of providing,outputting, transmitting, sending, or presenting analog or digital datacan be accomplished by transferring data as an input or output parameterof a function call, a parameter of an application programming interfaceor interprocess communication mechanism.

Although discussion above sets forth example implementations ofdescribed techniques, other architectures may be used to implementdescribed functionality, and are intended to be within scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, variousfunctions and responsibilities might be distributed and divided indifferent ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A computer-implemented method, comprising:generating a scene structure using at least a subset of a plurality ofrules from a rule set, the plurality of rules specifying relationshipsbetween types of objects in a scene; determining one or more parametervalues for one or more of the objects represented in the scenestructure; generating a scene graph based on the scene structure andincluding the parameter values; and providing the scene graph to arendering engine to render an image of the scene.
 2. Thecomputer-implemented method of claim 1, further comprising: providing,to the rendering engine, a library of object-related content to be usedto render the objects represented in the scene structure using the oneor more parameter values for the objects.
 3. The computer-implementedmethod of claim 1, further comprising: causing the rendering engine toinclude at least one of object labels or scene structure informationwith the image of the scene, wherein the image is capable of being usedas training data for training a neural network.
 4. Thecomputer-implemented method of claim 1, further comprising: receivinginput indicating that one of the rules or objects is not to be utilizedfor the scene; updating the scene graph to reflect the input; andproviding the updated scene graph to the rendering engine to render anupdated image of the scene.
 5. The computer-implemented method of claim1, further comprising: utilizing the rule set to generate a diverse setof scene graphs; and generating a virtual environment using the diverseset of scene graphs.
 6. The computer-implemented method of claim 5,wherein the virtual environment is a gaming environment, and wherein therendering engine is to use the diverse set of scene graphs to render oneor more images of the gaming environment for one or more players of agame corresponding to the gaming environment.
 7. Thecomputer-implemented method of claim 5, further comprising: utilizingthe virtual environment in one or more testing simulations for one ormore autonomous or semi-autonomous machines.
 8. The computer-implementedmethod of claim 1, wherein the scene structure is generated from therule set in an unsupervised manner without data annotation on one ormore input images.
 9. The computer-implemented method of claim 1,further comprising: assigning probabilities to rules of the rule set;and selecting the subset of the plurality of rules by sampling the rulesaccording to the probabilities.
 10. The computer-implemented method ofclaim 9, further comprising: determining a scene mask to indicate whichone or more rules of the rule set are able to be selected through thesampling.
 11. The computer-implemented method of claim 1, furthercomprising: generating the scene structure using an iterative process inwhich, at one or more of a plurality of time steps, a determination ismade whether to perform expansion for one or more object types in thescene structure, the object types being positioned at varying levels ina hierarchical scene structure.
 12. The computer-implemented method ofclaim 1, wherein a generative model is used to generate the scenestructure using the rule set.
 13. The computer-implemented method ofclaim 1, further comprising: generating a latent vector as input to thegenerative model, the latent vector having a length equal to a number ofrules in the rule set.
 14. A system, comprising: at least one processor;and memory including instructions that, when executed by the at leastone processor, cause the system to: generate a scene structure using atleast a subset of a plurality of rules from a rule set, the plurality ofrules specifying relationships between types of objects in a scene;determine one or more parameter values for one or more of the objectsrepresented in the scene structure; generate a scene graph based on thescene structure and including the parameter values; and provide thescene graph to a rendering engine to render an image of the scene. 15.The system of claim 14, wherein the instructions when executed furthercause the system to: cause the rendering engine to include at least oneof object labels or scene structure information with the image of thescene, wherein the image is capable of being used as training data fortraining a neural network.
 16. The system of claim 14, wherein theinstructions when executed further cause the system to: receive inputindicating that at least one of the rules or objects is not to beutilized for the scene; update the scene graph to reflect the input; andprovide the updated scene graph to the rendering engine to render anupdated image of the scene.
 17. The system of claim 14, wherein theinstructions when executed further cause the system to: utilize the ruleset to generate a diverse set of scene graphs; and generate a virtualenvironment using the diverse set of scene graphs.
 18. The system ofclaim 14, wherein the system comprises at least one of: a system forperforming graphical rendering operations; a system for performingsimulation operations; a system for performing simulation operations totest or validate autonomous machine applications; a system forperforming deep learning operations; a system implemented using an edgedevice; a system incorporating one or more Virtual Machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 19. Amethod for synthesizing training data, comprising: obtaining a rule setincluding a plurality of rules specifying relationships between types ofobjects in a scene; generating a plurality of scene structures based onthe plurality of rules; determining one or more parameter values foreach of the objects represented in the plurality of scene structures;generating a plurality of scene graphs based on the plurality of scenestructures and including the parameter values; and providing theplurality of scene graphs to a rendering engine to render a set ofimages including data for the objects represented in the images, the setof images representing training data for use in training one or moreneural networks.
 20. The method of claim 19, further comprising:providing, to the rendering engine, a library of object-related contentto be used to render the objects represented in the scene structureusing the one or more parameter values for the objects.
 21. The methodof claim 19, further comprising: receiving input indicating that one ofthe rules or objects is not to be utilized; updating the plurality ofscene graphs to reflect the input; and providing the updated scenegraphs to the rendering engine to render an updated set of images. 22.The method of claim 19, wherein the scene structure is generated fromthe rule set in an unsupervised manner without data annotation on one ormore input images.
 23. The method of claim 19, further comprising:assigning probabilities to rules of the rule set; and selecting a subsetof the plurality of rules for respective scene structures by samplingthe rules according to the probabilities.